Peter Adebayo Idowu Ph.D | Obafemi Awolowo University, Ile-Ife (original) (raw)
Papers by Peter Adebayo Idowu Ph.D
Research Anthology on Machine Learning Techniques, Methods, and Applications
This chapter was developed with a view to present a predictive model for the classification of th... more This chapter was developed with a view to present a predictive model for the classification of the level of CD4 count of HIV patients receiving ART/HAART treatment in Nigeria. Following the review of literature, the pre-determining factors for determining CD4 count were identified and validated by experts while historical data explaining the relationship between the factors and CD4 count level was collected. The predictive model for CD4 count level was formulated using C4.5 decision trees (DT), support vector machines (SVM), and the multi-layer perceptron (MLP) classifiers based on the identified factors which were formulated using WEKA software and validated. The results showed that decision trees algorithm revealed five (5) important variables, namely age group, white blood cell count, viral load, time of diagnosing HIV, and age of the patient. The MLP had the best performance with a value of 100% followed by the SVM with an accuracy of 91.1%, and both were observed to outperform ...
International Journal of Healthcare Information Systems and Informatics, 2021
This study presented a model to classify risk of hypertension using Adaptive Neuro-Fuzzy Inferenc... more This study presented a model to classify risk of hypertension using Adaptive Neuro-Fuzzy Inference System (ANFIS). In order to develop the model cardiologists from teaching hospitals in Nigeria were interviewed so as to identify required variables for classification. Structured questionnaires were used to elicit information about the risk factors and the associated risk of hypertension from respondents. The MATLAB ANFIS Toolbox was used to simulate the model. The result of this study revealed that there were 33 main variables identified for monitoring hypertension risk and they were in line with the WHO/ISH classification standard. The result showed that majority of the patients selected had very high risk (57.0%) of hypertension which consisted more than 50% of the patients selected followed by 19% representing patients with high risk of hypertension, followed by patients with medium risk of hypertension. In conclusion, the model assist healthcare professionals to have accurate dia...
This study was motivated at developing an ensemble of 3 supervised machine learning algorithms fo... more This study was motivated at developing an ensemble of 3 supervised machine learning algorithms for the assessment of the severity of sickle cell disease (SCD) among paediatric patients. The study collected data from a tertiary hospital in south-western Nigeria following the identification of variables required for assessing the severity of SCD. The study also adopted the use of 3 supervised machine learning algorithms namely: naive Bayes (NB), C4.5 decision trees (DT) and support vector machines (SVM) for creating the ensemble model using a 10-fold cross validation technique. The models were created by adopting the algorithms in isolation and in combination of 2 and 3 which were compared. The developed models were evaluated in order to present the model with the best performance. The results of the study showed that using an ensemble of DT and NB alone provided the best performance. The study has implications in presenting a model for improving the assessment of the severity of SCD ...
International journal of child health and human development, 2017
(ProQuest: ... denotes formulae omitted.)IntroductionEpidemic diseases have highly destructive ef... more (ProQuest: ... denotes formulae omitted.)IntroductionEpidemic diseases have highly destructive effects around the world and these diseases have affected both developed and developing nations. Disease epidemics are common in developing nations especially in Sub Saharan Africa in which Human Immunodeficiency Virus / Acquired Immunodeficiency Disease Syndrome (HIV/AIDS) is the most serious of all (1). HIV is one of the world's most serious health and development challenges (2). It is a type of virus called a retrovirus which infects humans when it comes in contact with tissues such as those that line the vagina, anal area, mouth, eyes or through a break in the skin (3), while Acquired Immunodeficiency Syndrome (AIDS) is the advanced stage of the retroviral infection that swept through sub-Saharan Africa with venom (4-5).Globally, HIV continues to be a very serious health issue facing the world (6). About 34 million (31.4 million-35.9 million) people were living with HIV at the end of 2011 and an estimated0.8% of adults aged 15-49 years worldwide are living with the virus, although the burden of the epidemic continues to vary considerably between countries and regions (6). In Sub-Saharan Africa, roughly 25 million people were living with HIV in 2012, accounting for nearly 70 percent of the global total. The epidemic has had widespread social and economic consequences, not only in the health sector but also in education, industry and the wider economy (8-9). The epidemic has had a heavy impact on education, school attendance drops as children become sick or return home to look after affected family members (10). Moreover, Sub-Saharan Africa remains the most severely affected, with nearly 1 in every 20 adults (4.9%) living with HIV; accounting for 69% of the people living with HIV worldwide. Although, the regional prevalence of HIV infection is nearly 25 times higher in sub-Saharan Africa than in Asia, almost 5 million people are living with HIV in South, South-East and East Asia combined and sub-Saharan Africa region is the most heavily affected region follow by the Caribbean, Eastern Europe and Central Asia, where 1.0% of adults were living with HIV as of 2011 (2).Nigeria is the most populous nation in Africa with an estimated population of over 160 million people. Government reports claim that over 300,000 Nigerians die yearly of complications arising from AIDS. Nigeria has the highest HIV populations in Africa with 5.7 million infected people. It is estimated that over 200,000 people die yearly in Nigeria as a result of HIV/AIDS (11). At present, there is no cure for HIV but it is being managed with antiretroviral drugs (ARV). There is optimal combination of ARV which is known as Highly Active Antiretroviral drug (HAART) (12-13). Antiretroviral therapy is the mechanism of treating retroviral infections with drugs. The drugs do not kill the virus but they slow down the growth of the virus (6). HAART refers to the use of combinations of various antiretroviral drugs with different mechanisms of action to treat HIV.The epidemic of HIV/AIDS affects two classes of people: the paediatric and the non-paediatric individual. The non-paediatric patients are patients above 15 years of age while the paediatric patients who form the main target of this research are patients whose age is less than 15 years (14). There are four distinct stages of HIV infection which includes: the primary HIV infection stage or clinical stage 1 which involves asymptomatic and acute retroviral syndrome; clinically asymptomatic stage or clinical stage 2 which involves moderate and unexplained weight loss (
International journal of child health and human development, 2017
(ProQuest: ... denotes formulae omitted.)IntroductionHIV/AIDS epidemic remains a global health ch... more (ProQuest: ... denotes formulae omitted.)IntroductionHIV/AIDS epidemic remains a global health challenge of unprecedented dimensions and a monumental threat to developmental progress (1). HIV/AIDS is a type of virus called a retrovirus which infects human when it comes in contact with tissues such as those that line the vagina, anal area, mouth, eyes or through a break in the skin (2), while Acquired Immunodeficiency Syndrome (AIDS) is the advanced stage of the retroviral infection that swept through sub-Saharan Africa with venom (3).This infection can cause seizures, fever, pneumonia, recurrent colds, diarrhea, dehydration and other problems that often result in extended hospital stay (4). HIV/AIDS continues to be a very serious health issue facing the world in which 34 million people were living with HIV at the end of 2011 and an estimated 0.8% of adults aged 15-49 years worldwide are living with the virus, although the burden of the epidemic continues to vary considerably between countries and regions (5, 6).In Sub-Saharan Africa, roughly 25 million people were living with HIV in 2012, accounting for nearly 70 percent of the global total of infected patients. The epidemic has both social and economic consequences, not only in the health sector but also in education, industry and the wider economy (7-8). Moreover, Sub-Saharan Africa remains most severely affected with nearly 1 in every 20 adults (4.9%) living with HIV which accounts for 69% of the people living with HIV worldwide at present.There is no cure for HIV but it is being managed with antiretroviral drugs (ARV) and Highly Active Antiretroviral drugs (HAART) which is the optimal combination of ARV (9,10). ARV does not kill the virus but slow down the growth of the virus (5, 7). Antiretroviral therapy (ART) and highly antiretroviral therapy (HAART) are the mechanisms for treating retroviral infections with drugs.The epidemic of HIV/AIDS affects two classes of people: the pediatric and the non-pediatric individuals. The non-Pediatric patients are patients above 15 years of age while the pediatric patients who form the main target of this research are patients whose age is less than 15 years (11).There are four distinct stages of HIV infection which includes: the primary HIV infection stage or clinical stage 1 which involves asymptomatic and acute retroviral syndrome ; clinically asymptomatic stage or clinical stage 2 which involves moderate and unexplained weight loss (There are different modes of transmission of this virus, one of which is mother-to-child transmission. About nine out of ten children exposed are infected with HIV during pregnancy, labour, delivery or while breastfeeding (14). Without treatment, 15-30 percent of babies born to HIV positive women are infected with the virus during pregnancy and delivery and a further 5-20 percent are also infected through breastfeeding (15). In high-income countries, preventive measures are undertaken to ensure that the transmission of HIV from Mother to Child is relatively rare and in cases where it occurs, a range of treatment options are undertaken so that the child can survive into adulthood. Blood transfusion is another route in which HIV infection can occur in medical setting (16). …
This study identified the Social Media that were most commonly used by students of tertiary insti... more This study identified the Social Media that were most commonly used by students of tertiary institutions across south-western Nigeria. Structured questionnaires were used to collect information about the Social Media that were adopted by students of tertiary institutions in Nigeria. The results of the study showed that social media adopted were: Facebook, Twitter, Instagram, SnapChat, WhatsApp, LinkedIn, WeChat, ResearchGate, Academia and Line; the most commonly used social media included: Facebook, SnapChat, Twitter and Instagram by at least 55% of the students while ResearchGate, Line, Academia, WeChat and LinkedIn accessed only when there were notifications. The results also showed that the earliest adopted social media included: Facebook in 2007 with 1 user, Twitter in 2009 with 3 users, Instagram and WhatsApp in 2010 with 1 and 8 users respectively. The impact of social media showed that at least 67% of the students suggested it had good impacts on their productivity and functi...
This study aims to understand the trend of social media adoption among youths especially undergra... more This study aims to understand the trend of social media adoption among youths especially undergraduates of Nigerian tertiary institutions. This study used a questionnaire for identifying the various social media platforms. Also, the study formulated a polynomial function for estimating the number of students who will adopt the use of social media platforms based on the number of years after the year of social media adoption. The results of the study show that the social media platform adopted by Nigerian undergraduate students include: Facebook, Twitter, Instagram, SnapChat, WhatsApp, LinkedIn, WeChat, ResearchGate, Academia and Line. The results show that the most commonly used platforms are: Facebook, SnapChat, Twitter and Instagram while the earliest adopted platforms include: Facebook in 2007, Twitter in 2009 including Instagram and WhatsApp in 2010. The results showed that the infusion model for the adoption of social media was formulated, using a polynomial function with the b...
Privacy and Security Challenges in Location Aware Computing, 2021
Currently in Nigeria, different crimes ranging from ethnic clashes, domestic violence, burglary, ... more Currently in Nigeria, different crimes ranging from ethnic clashes, domestic violence, burglary, financial fraud, kidnapping, pipe-line vandalism, and random killings by terrorist organizations, to mention a few, continue to plague the country. The conventional system of intelligence and crime record have failed to live up to the expectations as a result of limited security personnel, deficiency in effective information technology strategies, and infrastructures for gathering, storing, and analyzing data for accurate prediction, decision support, and prevention of crimes. There is presently no information system in Nigeria that provides a central database that is capable of storing the spatial distribution of various acts of terrorism based on the location where the crime is committed. This chapter presents the design of an information system that can be used by security agents for the storage and retrieval of criminal acts of terrorism in order to provide improved decision support ...
Computational Biology and Bioinformatics, 2020
International Journal of Theoretical and Applied Mathematics, 2020
This study aims to formulate a classification model which farmers can use to determine the suitab... more This study aims to formulate a classification model which farmers can use to determine the suitability of a land for supporting cultivation based on information about identified factors. Structured interview with farmers and agro-specialists were conducted in order to identify the factors associated with the classification of land suitability. Fuzzy membership function was used to formulate the input and output variables of the classification model for land suitability based on the risk factors identified. The model was simulated using MATLAB® R2015b-Fuzzy Logic Tool. The results showed that 7 risk factors were associated with the classification of the suitability of land for crop planting. The risk factors identified are annual rainfall, months of dry season, relative humidity, abundance of clay soil, abundance of sand soil, abundance of organic carbon and pH value of soil on land. 2 and 3 triangular membership functions were appropriate for the formulation of the linguistic variables of the factors using appropriate linguistic variables while the target suitability of land was formulated using four triangular membership functions for the linguistic variables unsuitable, fairly suitable, moderately suitable and highly suitable. 288 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the suitability of land for planting crops as the consequent part of each rule. This study concluded that based on the assessment of information about the factors associated with the classification of land suitability a reasonable conclusion can be made about the possible use of land.
International Journal of Computers in Clinical Practice, 2019
Anemia is a major cause of morbidity and mortality of SCD patients in many parts of the world wit... more Anemia is a major cause of morbidity and mortality of SCD patients in many parts of the world with the burden much higher in Sub Saharan Africa. This study developed an ensemble of machine learning algorithm for the prediction of the risk of anemia in pediatric SCD patients. Data for this study was collected from 115 pediatric SCD outpatients receiving treatment at a tertiary hospital in South-Western Nigeria. This study adopted a stack-ensemble model composed of deep neural network (DNN), multi-layer perceptron (MLP), and support vector machines (SVM) as base and meta-classifiers using the WEKA software. The ensemble models were compared following the stack-ensemble developed using SVM as a meta-classifier had the best performance with an accuracy of 72.7%. The study concluded that information about socio-demographic and clinical data can be used to assess the risk of anemia among SCD patients.
International Journal of Big Data and Analytics in Healthcare, 2020
This study developed a classification model for monitoring the risk of sexually transmitted disea... more This study developed a classification model for monitoring the risk of sexually transmitted diseases (STDs) among females using information about non-invasive risk factors. Structured interview with physicians was done in order to identify the risk factors that are associated with the risk of STDs in Nigeria. The model was simulated using the fuzzy logic toolbox accessible in the MATLAB® R2015a Software. The results showed that nine non-invasive risk factors were associated with the risk of STDs among female patients in Nigeria. Two, three, and four triangular membership functions were appropriate for the formulation of the linguistic variables of the factors while the target risk was formulated using four triangular membership functions for the linguistic variables namely no risk, low risk, moderate risk, and high risk. The study concluded that the fuzzy logic model approach was adequate for predicting the risk of STDs based on the knowledge of the risk factors.
American Journal of Pediatrics, 2019
Neonatal jaundice is a yellowish discoloration of the white part of the eyes and skin in a newbor... more Neonatal jaundice is a yellowish discoloration of the white part of the eyes and skin in a newborn baby due to high bilirubin levels. An early diagnosis of the severity of neonatal jaundice using machine learning will decrease neonates' likelihood of developing complications. The study elicited knowledge on the variables that are associated with the severity of neonatal jaundice and collected relevant data from a tertiary hospital in southwestern Nigeria. The study formulated the predictive model for the severity of neonatal jaundice based on the variables identified using deep learning with multi-layer perceptron (MLP) classifier for varying number of epochs. The results of the study showed that using the deep learning with MLP classifier and 5 epochs had the lowest error rate however with the highest build time and provided a better model compared to the use of the other number of epochs. The study concluded that the using deep learning with MLP classifier and 5 epochs, the development of the classification model for the severity of neonatal jaundice patients receiving treatment was more effective due to its ability to understand the relationship between the attributes and their respective target class labels.
International Journal of Biomedical Science and Engineering, 2019
This study predict the risk of thalassemia in all age groups based on identified risk of thalasse... more This study predict the risk of thalassemia in all age groups based on identified risk of thalassemia. Knowledge about the risk factors for thalassemia was identified using structural interview with experienced medical personnel and questionnaire which was used to collect empirical medical database on the parameters. Supervised machine learning algorithms was used to formulate the predictive model for risk of thalassemia using the parameters and data identified and collected. The predictive model for the risk of thalassemia was simulated using the Waikato Environment for Knowledge Analysis (WEKA). The simulated model was validated using the historical data collected from the hospitals explaining the parameters and the risk of Thalassemia. The results of the study showed that following the collection of data from 51 patients, the parameters identified included demographic variables like gender, age, marital status, ethnicity and social class while the clinical variables included family history, spleen enlargement, diabetes, urine colour changes and parent carriers while the distribution of the risk was 43% no cases, 10% low cases, 16% moderate cases and 31% high cases. The study concluded that using the multi-layer perceptron for the prediction of Thalassemia will improve the decision making process within the healthcare service concerning Thalassemia.
International Journal of E-Health and Medical Communications, 2019
This article presents a predictive model that can be used for the early detection of Type 2 Diabe... more This article presents a predictive model that can be used for the early detection of Type 2 Diabetes Mellitus using fuzzy logic. In order to formulate the model, risk factors associated with the risk of T2DM were elicited. The predictive model was formulated using fuzzy triangular membership functions following which the rules needed for the inference engine was elicited from experts. The model was simulated using the MATLAB Fuzzy logic Toolbox. The results of the study showed that the sensitivity of 11.67% and 100% precision for the low risk was recorded for both cases, specificity of 41.67% compared to 48.33% for the moderate risk, while there was 0% and 13.33% for the high risk. In conclusion, this model will help the doctor to know what course of preventive actions for a patient with high risk and what advice to give to those with low and moderate risk so that the occurrences of the diseases can be prevented altogether and thereby reducing the number of people dying from Type 2 ...
International Journal of Computer Applications, 2019
This paper focused on the development of a predictive model for the classification of the risk of... more This paper focused on the development of a predictive model for the classification of the risk of kidney stones in Nigerian using data mining techniques based on historical information elicited about the risk of kidney stones among Nigerians. Following the identification of the risk factors of kidney stone from experienced endocrinologists, structured questionnaires were used to collect information about the risk factors and the associated risk of kidney stones from selected respondents.
American Journal of Mathematical and Computer Modelling, 2018
This paper identified the risk factors for environmental health related diseases and formulated a... more This paper identified the risk factors for environmental health related diseases and formulated a fuzzy logic based predictive model based on the identified variables. Related literatures were reviewed so as to understand the body of knowledge surrounding environmental health related diseases and their corresponding risk factors, interviews with community health officers were conducted in order to validate the identified variables. Fuzzy logic was used to formulate the predictive model using Matlab Fuzzy logic tool box. Data was collected from five different states in Nigeria. The result showed that there are cases of environmental related diseases in the areas where there is no potable water and in locations that lack good toilet facilities. In the areas where there is no toilet facility or where bucket and bush are used as toilet, there are always cases of cholera. In these areas during the rainy season cholera outbreaks are common occurrences. All these points to fact that, if there is a good environmental health tracking system with predictive features, then environmental health officers would be able to easily monitor, manage and track any area which may be prone to any of these environmental health diseases.
International Journal of Computers in Clinical Practice, 2017
In this article, an IT infusion model was developed for the Nigerian health sector using teaching... more In this article, an IT infusion model was developed for the Nigerian health sector using teaching hospitals in Nigeria. The different IT technologies used by medical practitioners were identified. Structured questionnaires were used to elicit knowledge from respondents selected from teaching hospitals located in Nigeria. The results showed that the nurses and doctors with less than 5 years working experience were common users of IT. The results for the IT infusion showed that the earliest IT component in the health sector was the personal computer since the year 1994 followed by mobile phone and search engines by the year 1996 with the projector infused by the year 2001. The infusion model for the total number of users for each IT component was formulated using polynomial functions of degree, n with respect to t – the number of years after the base year of infusion of IT device. The IT infusion model developed in this paper can be used to forecast the future growth of IT within the ...
International Journal of Biomedical and Clinical Engineering, 2017
This paper presents an online monitoring system for the storage and retrieval of physiological da... more This paper presents an online monitoring system for the storage and retrieval of physiological data from neonates admitted into the Neonatal intensive care units (NICU) of Obafemi Awolowo University Teaching Hospital, Ile-Ife, Nigeria. In order to develop this system, the requirements of the proposed system were identified and analyzed as system and user requirements independently and the requirements were designed using the Unified Modeling Language (UML) tools. The system was implemented using Web 2.0 technologies such as, the hypertext markup language (HTML), the cascading styling sheets (CSS), PHP and MySQL. With the system, storage and retrieval of information by the nurses and any authorized users will be easy.
Handbook of Research on Healthcare Administration and Management
According to WHO, there are 60-80 million infertile couples worldwide with the highest incidence ... more According to WHO, there are 60-80 million infertile couples worldwide with the highest incidence in some regions of Sub-Saharan Africa. The social stigma of infertility weighs especially heavily on women, who bear the sole blame for barren marriages in many developing countries and may face divorce as a result. Interviews were conducted with gynecologists at one of the Teaching Hospitals in Nigeria in order to identify likelihood variables for infertility. 14 risk factors were identified and data collected from 39 patients from the hospital was pre-processed and the variables used to formulate the predictive model for the likelihood of infertility in women using three different decision trees algorithms. The predictive model was simulated using WEKA environment. The results revealed that C4.5 algorithm had the highest accuracy of 74.4% while the least performance was for the random tree algorithm with a value of 53.8%. This chapter presents a predictive model which can assist gynecologists in making more objective decisions concerning infertility likelihood.
Research Anthology on Machine Learning Techniques, Methods, and Applications
This chapter was developed with a view to present a predictive model for the classification of th... more This chapter was developed with a view to present a predictive model for the classification of the level of CD4 count of HIV patients receiving ART/HAART treatment in Nigeria. Following the review of literature, the pre-determining factors for determining CD4 count were identified and validated by experts while historical data explaining the relationship between the factors and CD4 count level was collected. The predictive model for CD4 count level was formulated using C4.5 decision trees (DT), support vector machines (SVM), and the multi-layer perceptron (MLP) classifiers based on the identified factors which were formulated using WEKA software and validated. The results showed that decision trees algorithm revealed five (5) important variables, namely age group, white blood cell count, viral load, time of diagnosing HIV, and age of the patient. The MLP had the best performance with a value of 100% followed by the SVM with an accuracy of 91.1%, and both were observed to outperform ...
International Journal of Healthcare Information Systems and Informatics, 2021
This study presented a model to classify risk of hypertension using Adaptive Neuro-Fuzzy Inferenc... more This study presented a model to classify risk of hypertension using Adaptive Neuro-Fuzzy Inference System (ANFIS). In order to develop the model cardiologists from teaching hospitals in Nigeria were interviewed so as to identify required variables for classification. Structured questionnaires were used to elicit information about the risk factors and the associated risk of hypertension from respondents. The MATLAB ANFIS Toolbox was used to simulate the model. The result of this study revealed that there were 33 main variables identified for monitoring hypertension risk and they were in line with the WHO/ISH classification standard. The result showed that majority of the patients selected had very high risk (57.0%) of hypertension which consisted more than 50% of the patients selected followed by 19% representing patients with high risk of hypertension, followed by patients with medium risk of hypertension. In conclusion, the model assist healthcare professionals to have accurate dia...
This study was motivated at developing an ensemble of 3 supervised machine learning algorithms fo... more This study was motivated at developing an ensemble of 3 supervised machine learning algorithms for the assessment of the severity of sickle cell disease (SCD) among paediatric patients. The study collected data from a tertiary hospital in south-western Nigeria following the identification of variables required for assessing the severity of SCD. The study also adopted the use of 3 supervised machine learning algorithms namely: naive Bayes (NB), C4.5 decision trees (DT) and support vector machines (SVM) for creating the ensemble model using a 10-fold cross validation technique. The models were created by adopting the algorithms in isolation and in combination of 2 and 3 which were compared. The developed models were evaluated in order to present the model with the best performance. The results of the study showed that using an ensemble of DT and NB alone provided the best performance. The study has implications in presenting a model for improving the assessment of the severity of SCD ...
International journal of child health and human development, 2017
(ProQuest: ... denotes formulae omitted.)IntroductionEpidemic diseases have highly destructive ef... more (ProQuest: ... denotes formulae omitted.)IntroductionEpidemic diseases have highly destructive effects around the world and these diseases have affected both developed and developing nations. Disease epidemics are common in developing nations especially in Sub Saharan Africa in which Human Immunodeficiency Virus / Acquired Immunodeficiency Disease Syndrome (HIV/AIDS) is the most serious of all (1). HIV is one of the world's most serious health and development challenges (2). It is a type of virus called a retrovirus which infects humans when it comes in contact with tissues such as those that line the vagina, anal area, mouth, eyes or through a break in the skin (3), while Acquired Immunodeficiency Syndrome (AIDS) is the advanced stage of the retroviral infection that swept through sub-Saharan Africa with venom (4-5).Globally, HIV continues to be a very serious health issue facing the world (6). About 34 million (31.4 million-35.9 million) people were living with HIV at the end of 2011 and an estimated0.8% of adults aged 15-49 years worldwide are living with the virus, although the burden of the epidemic continues to vary considerably between countries and regions (6). In Sub-Saharan Africa, roughly 25 million people were living with HIV in 2012, accounting for nearly 70 percent of the global total. The epidemic has had widespread social and economic consequences, not only in the health sector but also in education, industry and the wider economy (8-9). The epidemic has had a heavy impact on education, school attendance drops as children become sick or return home to look after affected family members (10). Moreover, Sub-Saharan Africa remains the most severely affected, with nearly 1 in every 20 adults (4.9%) living with HIV; accounting for 69% of the people living with HIV worldwide. Although, the regional prevalence of HIV infection is nearly 25 times higher in sub-Saharan Africa than in Asia, almost 5 million people are living with HIV in South, South-East and East Asia combined and sub-Saharan Africa region is the most heavily affected region follow by the Caribbean, Eastern Europe and Central Asia, where 1.0% of adults were living with HIV as of 2011 (2).Nigeria is the most populous nation in Africa with an estimated population of over 160 million people. Government reports claim that over 300,000 Nigerians die yearly of complications arising from AIDS. Nigeria has the highest HIV populations in Africa with 5.7 million infected people. It is estimated that over 200,000 people die yearly in Nigeria as a result of HIV/AIDS (11). At present, there is no cure for HIV but it is being managed with antiretroviral drugs (ARV). There is optimal combination of ARV which is known as Highly Active Antiretroviral drug (HAART) (12-13). Antiretroviral therapy is the mechanism of treating retroviral infections with drugs. The drugs do not kill the virus but they slow down the growth of the virus (6). HAART refers to the use of combinations of various antiretroviral drugs with different mechanisms of action to treat HIV.The epidemic of HIV/AIDS affects two classes of people: the paediatric and the non-paediatric individual. The non-paediatric patients are patients above 15 years of age while the paediatric patients who form the main target of this research are patients whose age is less than 15 years (14). There are four distinct stages of HIV infection which includes: the primary HIV infection stage or clinical stage 1 which involves asymptomatic and acute retroviral syndrome; clinically asymptomatic stage or clinical stage 2 which involves moderate and unexplained weight loss (
International journal of child health and human development, 2017
(ProQuest: ... denotes formulae omitted.)IntroductionHIV/AIDS epidemic remains a global health ch... more (ProQuest: ... denotes formulae omitted.)IntroductionHIV/AIDS epidemic remains a global health challenge of unprecedented dimensions and a monumental threat to developmental progress (1). HIV/AIDS is a type of virus called a retrovirus which infects human when it comes in contact with tissues such as those that line the vagina, anal area, mouth, eyes or through a break in the skin (2), while Acquired Immunodeficiency Syndrome (AIDS) is the advanced stage of the retroviral infection that swept through sub-Saharan Africa with venom (3).This infection can cause seizures, fever, pneumonia, recurrent colds, diarrhea, dehydration and other problems that often result in extended hospital stay (4). HIV/AIDS continues to be a very serious health issue facing the world in which 34 million people were living with HIV at the end of 2011 and an estimated 0.8% of adults aged 15-49 years worldwide are living with the virus, although the burden of the epidemic continues to vary considerably between countries and regions (5, 6).In Sub-Saharan Africa, roughly 25 million people were living with HIV in 2012, accounting for nearly 70 percent of the global total of infected patients. The epidemic has both social and economic consequences, not only in the health sector but also in education, industry and the wider economy (7-8). Moreover, Sub-Saharan Africa remains most severely affected with nearly 1 in every 20 adults (4.9%) living with HIV which accounts for 69% of the people living with HIV worldwide at present.There is no cure for HIV but it is being managed with antiretroviral drugs (ARV) and Highly Active Antiretroviral drugs (HAART) which is the optimal combination of ARV (9,10). ARV does not kill the virus but slow down the growth of the virus (5, 7). Antiretroviral therapy (ART) and highly antiretroviral therapy (HAART) are the mechanisms for treating retroviral infections with drugs.The epidemic of HIV/AIDS affects two classes of people: the pediatric and the non-pediatric individuals. The non-Pediatric patients are patients above 15 years of age while the pediatric patients who form the main target of this research are patients whose age is less than 15 years (11).There are four distinct stages of HIV infection which includes: the primary HIV infection stage or clinical stage 1 which involves asymptomatic and acute retroviral syndrome ; clinically asymptomatic stage or clinical stage 2 which involves moderate and unexplained weight loss (There are different modes of transmission of this virus, one of which is mother-to-child transmission. About nine out of ten children exposed are infected with HIV during pregnancy, labour, delivery or while breastfeeding (14). Without treatment, 15-30 percent of babies born to HIV positive women are infected with the virus during pregnancy and delivery and a further 5-20 percent are also infected through breastfeeding (15). In high-income countries, preventive measures are undertaken to ensure that the transmission of HIV from Mother to Child is relatively rare and in cases where it occurs, a range of treatment options are undertaken so that the child can survive into adulthood. Blood transfusion is another route in which HIV infection can occur in medical setting (16). …
This study identified the Social Media that were most commonly used by students of tertiary insti... more This study identified the Social Media that were most commonly used by students of tertiary institutions across south-western Nigeria. Structured questionnaires were used to collect information about the Social Media that were adopted by students of tertiary institutions in Nigeria. The results of the study showed that social media adopted were: Facebook, Twitter, Instagram, SnapChat, WhatsApp, LinkedIn, WeChat, ResearchGate, Academia and Line; the most commonly used social media included: Facebook, SnapChat, Twitter and Instagram by at least 55% of the students while ResearchGate, Line, Academia, WeChat and LinkedIn accessed only when there were notifications. The results also showed that the earliest adopted social media included: Facebook in 2007 with 1 user, Twitter in 2009 with 3 users, Instagram and WhatsApp in 2010 with 1 and 8 users respectively. The impact of social media showed that at least 67% of the students suggested it had good impacts on their productivity and functi...
This study aims to understand the trend of social media adoption among youths especially undergra... more This study aims to understand the trend of social media adoption among youths especially undergraduates of Nigerian tertiary institutions. This study used a questionnaire for identifying the various social media platforms. Also, the study formulated a polynomial function for estimating the number of students who will adopt the use of social media platforms based on the number of years after the year of social media adoption. The results of the study show that the social media platform adopted by Nigerian undergraduate students include: Facebook, Twitter, Instagram, SnapChat, WhatsApp, LinkedIn, WeChat, ResearchGate, Academia and Line. The results show that the most commonly used platforms are: Facebook, SnapChat, Twitter and Instagram while the earliest adopted platforms include: Facebook in 2007, Twitter in 2009 including Instagram and WhatsApp in 2010. The results showed that the infusion model for the adoption of social media was formulated, using a polynomial function with the b...
Privacy and Security Challenges in Location Aware Computing, 2021
Currently in Nigeria, different crimes ranging from ethnic clashes, domestic violence, burglary, ... more Currently in Nigeria, different crimes ranging from ethnic clashes, domestic violence, burglary, financial fraud, kidnapping, pipe-line vandalism, and random killings by terrorist organizations, to mention a few, continue to plague the country. The conventional system of intelligence and crime record have failed to live up to the expectations as a result of limited security personnel, deficiency in effective information technology strategies, and infrastructures for gathering, storing, and analyzing data for accurate prediction, decision support, and prevention of crimes. There is presently no information system in Nigeria that provides a central database that is capable of storing the spatial distribution of various acts of terrorism based on the location where the crime is committed. This chapter presents the design of an information system that can be used by security agents for the storage and retrieval of criminal acts of terrorism in order to provide improved decision support ...
Computational Biology and Bioinformatics, 2020
International Journal of Theoretical and Applied Mathematics, 2020
This study aims to formulate a classification model which farmers can use to determine the suitab... more This study aims to formulate a classification model which farmers can use to determine the suitability of a land for supporting cultivation based on information about identified factors. Structured interview with farmers and agro-specialists were conducted in order to identify the factors associated with the classification of land suitability. Fuzzy membership function was used to formulate the input and output variables of the classification model for land suitability based on the risk factors identified. The model was simulated using MATLAB® R2015b-Fuzzy Logic Tool. The results showed that 7 risk factors were associated with the classification of the suitability of land for crop planting. The risk factors identified are annual rainfall, months of dry season, relative humidity, abundance of clay soil, abundance of sand soil, abundance of organic carbon and pH value of soil on land. 2 and 3 triangular membership functions were appropriate for the formulation of the linguistic variables of the factors using appropriate linguistic variables while the target suitability of land was formulated using four triangular membership functions for the linguistic variables unsuitable, fairly suitable, moderately suitable and highly suitable. 288 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the suitability of land for planting crops as the consequent part of each rule. This study concluded that based on the assessment of information about the factors associated with the classification of land suitability a reasonable conclusion can be made about the possible use of land.
International Journal of Computers in Clinical Practice, 2019
Anemia is a major cause of morbidity and mortality of SCD patients in many parts of the world wit... more Anemia is a major cause of morbidity and mortality of SCD patients in many parts of the world with the burden much higher in Sub Saharan Africa. This study developed an ensemble of machine learning algorithm for the prediction of the risk of anemia in pediatric SCD patients. Data for this study was collected from 115 pediatric SCD outpatients receiving treatment at a tertiary hospital in South-Western Nigeria. This study adopted a stack-ensemble model composed of deep neural network (DNN), multi-layer perceptron (MLP), and support vector machines (SVM) as base and meta-classifiers using the WEKA software. The ensemble models were compared following the stack-ensemble developed using SVM as a meta-classifier had the best performance with an accuracy of 72.7%. The study concluded that information about socio-demographic and clinical data can be used to assess the risk of anemia among SCD patients.
International Journal of Big Data and Analytics in Healthcare, 2020
This study developed a classification model for monitoring the risk of sexually transmitted disea... more This study developed a classification model for monitoring the risk of sexually transmitted diseases (STDs) among females using information about non-invasive risk factors. Structured interview with physicians was done in order to identify the risk factors that are associated with the risk of STDs in Nigeria. The model was simulated using the fuzzy logic toolbox accessible in the MATLAB® R2015a Software. The results showed that nine non-invasive risk factors were associated with the risk of STDs among female patients in Nigeria. Two, three, and four triangular membership functions were appropriate for the formulation of the linguistic variables of the factors while the target risk was formulated using four triangular membership functions for the linguistic variables namely no risk, low risk, moderate risk, and high risk. The study concluded that the fuzzy logic model approach was adequate for predicting the risk of STDs based on the knowledge of the risk factors.
American Journal of Pediatrics, 2019
Neonatal jaundice is a yellowish discoloration of the white part of the eyes and skin in a newbor... more Neonatal jaundice is a yellowish discoloration of the white part of the eyes and skin in a newborn baby due to high bilirubin levels. An early diagnosis of the severity of neonatal jaundice using machine learning will decrease neonates' likelihood of developing complications. The study elicited knowledge on the variables that are associated with the severity of neonatal jaundice and collected relevant data from a tertiary hospital in southwestern Nigeria. The study formulated the predictive model for the severity of neonatal jaundice based on the variables identified using deep learning with multi-layer perceptron (MLP) classifier for varying number of epochs. The results of the study showed that using the deep learning with MLP classifier and 5 epochs had the lowest error rate however with the highest build time and provided a better model compared to the use of the other number of epochs. The study concluded that the using deep learning with MLP classifier and 5 epochs, the development of the classification model for the severity of neonatal jaundice patients receiving treatment was more effective due to its ability to understand the relationship between the attributes and their respective target class labels.
International Journal of Biomedical Science and Engineering, 2019
This study predict the risk of thalassemia in all age groups based on identified risk of thalasse... more This study predict the risk of thalassemia in all age groups based on identified risk of thalassemia. Knowledge about the risk factors for thalassemia was identified using structural interview with experienced medical personnel and questionnaire which was used to collect empirical medical database on the parameters. Supervised machine learning algorithms was used to formulate the predictive model for risk of thalassemia using the parameters and data identified and collected. The predictive model for the risk of thalassemia was simulated using the Waikato Environment for Knowledge Analysis (WEKA). The simulated model was validated using the historical data collected from the hospitals explaining the parameters and the risk of Thalassemia. The results of the study showed that following the collection of data from 51 patients, the parameters identified included demographic variables like gender, age, marital status, ethnicity and social class while the clinical variables included family history, spleen enlargement, diabetes, urine colour changes and parent carriers while the distribution of the risk was 43% no cases, 10% low cases, 16% moderate cases and 31% high cases. The study concluded that using the multi-layer perceptron for the prediction of Thalassemia will improve the decision making process within the healthcare service concerning Thalassemia.
International Journal of E-Health and Medical Communications, 2019
This article presents a predictive model that can be used for the early detection of Type 2 Diabe... more This article presents a predictive model that can be used for the early detection of Type 2 Diabetes Mellitus using fuzzy logic. In order to formulate the model, risk factors associated with the risk of T2DM were elicited. The predictive model was formulated using fuzzy triangular membership functions following which the rules needed for the inference engine was elicited from experts. The model was simulated using the MATLAB Fuzzy logic Toolbox. The results of the study showed that the sensitivity of 11.67% and 100% precision for the low risk was recorded for both cases, specificity of 41.67% compared to 48.33% for the moderate risk, while there was 0% and 13.33% for the high risk. In conclusion, this model will help the doctor to know what course of preventive actions for a patient with high risk and what advice to give to those with low and moderate risk so that the occurrences of the diseases can be prevented altogether and thereby reducing the number of people dying from Type 2 ...
International Journal of Computer Applications, 2019
This paper focused on the development of a predictive model for the classification of the risk of... more This paper focused on the development of a predictive model for the classification of the risk of kidney stones in Nigerian using data mining techniques based on historical information elicited about the risk of kidney stones among Nigerians. Following the identification of the risk factors of kidney stone from experienced endocrinologists, structured questionnaires were used to collect information about the risk factors and the associated risk of kidney stones from selected respondents.
American Journal of Mathematical and Computer Modelling, 2018
This paper identified the risk factors for environmental health related diseases and formulated a... more This paper identified the risk factors for environmental health related diseases and formulated a fuzzy logic based predictive model based on the identified variables. Related literatures were reviewed so as to understand the body of knowledge surrounding environmental health related diseases and their corresponding risk factors, interviews with community health officers were conducted in order to validate the identified variables. Fuzzy logic was used to formulate the predictive model using Matlab Fuzzy logic tool box. Data was collected from five different states in Nigeria. The result showed that there are cases of environmental related diseases in the areas where there is no potable water and in locations that lack good toilet facilities. In the areas where there is no toilet facility or where bucket and bush are used as toilet, there are always cases of cholera. In these areas during the rainy season cholera outbreaks are common occurrences. All these points to fact that, if there is a good environmental health tracking system with predictive features, then environmental health officers would be able to easily monitor, manage and track any area which may be prone to any of these environmental health diseases.
International Journal of Computers in Clinical Practice, 2017
In this article, an IT infusion model was developed for the Nigerian health sector using teaching... more In this article, an IT infusion model was developed for the Nigerian health sector using teaching hospitals in Nigeria. The different IT technologies used by medical practitioners were identified. Structured questionnaires were used to elicit knowledge from respondents selected from teaching hospitals located in Nigeria. The results showed that the nurses and doctors with less than 5 years working experience were common users of IT. The results for the IT infusion showed that the earliest IT component in the health sector was the personal computer since the year 1994 followed by mobile phone and search engines by the year 1996 with the projector infused by the year 2001. The infusion model for the total number of users for each IT component was formulated using polynomial functions of degree, n with respect to t – the number of years after the base year of infusion of IT device. The IT infusion model developed in this paper can be used to forecast the future growth of IT within the ...
International Journal of Biomedical and Clinical Engineering, 2017
This paper presents an online monitoring system for the storage and retrieval of physiological da... more This paper presents an online monitoring system for the storage and retrieval of physiological data from neonates admitted into the Neonatal intensive care units (NICU) of Obafemi Awolowo University Teaching Hospital, Ile-Ife, Nigeria. In order to develop this system, the requirements of the proposed system were identified and analyzed as system and user requirements independently and the requirements were designed using the Unified Modeling Language (UML) tools. The system was implemented using Web 2.0 technologies such as, the hypertext markup language (HTML), the cascading styling sheets (CSS), PHP and MySQL. With the system, storage and retrieval of information by the nurses and any authorized users will be easy.
Handbook of Research on Healthcare Administration and Management
According to WHO, there are 60-80 million infertile couples worldwide with the highest incidence ... more According to WHO, there are 60-80 million infertile couples worldwide with the highest incidence in some regions of Sub-Saharan Africa. The social stigma of infertility weighs especially heavily on women, who bear the sole blame for barren marriages in many developing countries and may face divorce as a result. Interviews were conducted with gynecologists at one of the Teaching Hospitals in Nigeria in order to identify likelihood variables for infertility. 14 risk factors were identified and data collected from 39 patients from the hospital was pre-processed and the variables used to formulate the predictive model for the likelihood of infertility in women using three different decision trees algorithms. The predictive model was simulated using WEKA environment. The results revealed that C4.5 algorithm had the highest accuracy of 74.4% while the least performance was for the random tree algorithm with a value of 53.8%. This chapter presents a predictive model which can assist gynecologists in making more objective decisions concerning infertility likelihood.