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Today, heart disease is a severe hazard to one's health that may result in death or a long-term i... more Today, heart disease is a severe hazard to one's health that may result in death or a long-term impairment because of the complicated blend of clinical and pathological evidence required to diagnose it. Despite the fact that medical diagnosis is a complex activity that plays a critical role in saving human lives, there is a dearth of appropriate tools to detect hidden linkages and patterns in electronic health data. Computer-based automated decision support systems are needed to lower the costs of clinical testing because of this complexity. " In this study, we provide a method for predicting the presence of cardiac disease based on clinical data collected from patients. In this study, the primary goal is to develop a predictive model for heart disease based on a combination of characteristics (risk factors). Different machine learning classification strategies will be deployed and evaluated based on conventional performance metrics such as accuracy in order to compare different machine learning algorithms.
Diabetes Mellitus is among critical diseases and lots of people are suffering from this disease. ... more Diabetes Mellitus is among critical diseases and lots of people are suffering from this disease. Age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, high blood pressure, etc. can cause Diabetes Mellitus. People having diabetes have high risk of diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. Current practice in hospital is to collect required information for diabetes diagnosis through various tests and appropriate treatment is provided based on diagnosis. Big Data Analytics plays an significant role in healthcare industries. Healthcare industries have large volume databases. Using big data analytics one can study huge datasets and find hidden information, hidden patterns to discover knowledge from the data and predict outcomes accordingly. In existing method, the classification and prediction accuracy is not so high. In this paper, we have proposed a diabetes prediction model for better classification of diabetes which includes few external factors responsible for diabetes along with regular factors like Glucose, BMI, Age, Insulin, etc. Classification accuracy is boosted with new dataset compared to existing dataset. Further with imposed a pipeline model for diabetes prediction intended towards improving the accuracy of classification.
In recent times, Heart Disease prediction is one of the most complicated tasks in medical field. ... more In recent times, Heart Disease prediction is one of the most complicated tasks in medical field. In the modern era, approximately one person dies per minute due to heart disease. Data science plays a crucial role in processing huge amount of data in the field of healthcare. As heart disease prediction is a complex task, there is a need to automate the prediction process to avoid risks associated with it and alert the patient well in advance. This paper makes use of heart disease dataset available in UCI machine learning repository. The proposed work predicts the chances of Heart Disease and classifies patient's risk level by implementing different data mining techniques such as Naive Bayes, Decision Tree, Logistic Regression and Random Forest. Thus, this paper presents a comparative study by analysing the performance of different machine learning algorithms. The trial results verify that Random Forest algorithm has achieved the highest accuracy of 90.16% compared to other ML algorithms implemented.
View the article online for updates and enhancements. You may also like ELM driven divertor targe... more View the article online for updates and enhancements. You may also like ELM driven divertor target currents on TCV R.A. Pitts, S. Alberti, P. Blanchard et al.-Progress and issues in understanding the physics of ELM dynamics, ELM mitigation, and ELM control N Oyama-Plasma shaping and its impact on the pedestal of ASDEX Upgrade: edge stability and inter-ELM dynamics at varied triangularity F.M. Laggner, E. Wolfrum, M. Cavedon et al.
Papers by CHARI B O R U BERISO
International Journal of Innovative Technology and Exploring Engineering, 2019
Heart disease is a common problem which can be very severe in old ages and also in people not hav... more Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basi...
Today, heart disease is a severe hazard to one's health that may result in death or a long-term i... more Today, heart disease is a severe hazard to one's health that may result in death or a long-term impairment because of the complicated blend of clinical and pathological evidence required to diagnose it. Despite the fact that medical diagnosis is a complex activity that plays a critical role in saving human lives, there is a dearth of appropriate tools to detect hidden linkages and patterns in electronic health data. Computer-based automated decision support systems are needed to lower the costs of clinical testing because of this complexity. " In this study, we provide a method for predicting the presence of cardiac disease based on clinical data collected from patients. In this study, the primary goal is to develop a predictive model for heart disease based on a combination of characteristics (risk factors). Different machine learning classification strategies will be deployed and evaluated based on conventional performance metrics such as accuracy in order to compare different machine learning algorithms.
Diabetes Mellitus is among critical diseases and lots of people are suffering from this disease. ... more Diabetes Mellitus is among critical diseases and lots of people are suffering from this disease. Age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, high blood pressure, etc. can cause Diabetes Mellitus. People having diabetes have high risk of diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. Current practice in hospital is to collect required information for diabetes diagnosis through various tests and appropriate treatment is provided based on diagnosis. Big Data Analytics plays an significant role in healthcare industries. Healthcare industries have large volume databases. Using big data analytics one can study huge datasets and find hidden information, hidden patterns to discover knowledge from the data and predict outcomes accordingly. In existing method, the classification and prediction accuracy is not so high. In this paper, we have proposed a diabetes prediction model for better classification of diabetes which includes few external factors responsible for diabetes along with regular factors like Glucose, BMI, Age, Insulin, etc. Classification accuracy is boosted with new dataset compared to existing dataset. Further with imposed a pipeline model for diabetes prediction intended towards improving the accuracy of classification.
In recent times, Heart Disease prediction is one of the most complicated tasks in medical field. ... more In recent times, Heart Disease prediction is one of the most complicated tasks in medical field. In the modern era, approximately one person dies per minute due to heart disease. Data science plays a crucial role in processing huge amount of data in the field of healthcare. As heart disease prediction is a complex task, there is a need to automate the prediction process to avoid risks associated with it and alert the patient well in advance. This paper makes use of heart disease dataset available in UCI machine learning repository. The proposed work predicts the chances of Heart Disease and classifies patient's risk level by implementing different data mining techniques such as Naive Bayes, Decision Tree, Logistic Regression and Random Forest. Thus, this paper presents a comparative study by analysing the performance of different machine learning algorithms. The trial results verify that Random Forest algorithm has achieved the highest accuracy of 90.16% compared to other ML algorithms implemented.
View the article online for updates and enhancements. You may also like ELM driven divertor targe... more View the article online for updates and enhancements. You may also like ELM driven divertor target currents on TCV R.A. Pitts, S. Alberti, P. Blanchard et al.-Progress and issues in understanding the physics of ELM dynamics, ELM mitigation, and ELM control N Oyama-Plasma shaping and its impact on the pedestal of ASDEX Upgrade: edge stability and inter-ELM dynamics at varied triangularity F.M. Laggner, E. Wolfrum, M. Cavedon et al.
International Journal of Innovative Technology and Exploring Engineering, 2019
Heart disease is a common problem which can be very severe in old ages and also in people not hav... more Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basi...