Applying a novel combination of techniques to develop a predictive model for diabetes complications (original) (raw)
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Eastern Mediterranean Health Journal
Background: Type 2 diabetes mellitus (T2DM) is a metabolic disease with complex causes, manifestations, complications and management. Understanding the wide range of risk factors for T2DM can facilitate diagnosis, proper classification and cost-effective management of the disease. Aims: To compare the power of an artificial neural network (ANN) and logistic regression in identifying T2DM risk factors. Methods: This descriptive and analytical study was conducted in 2013. The study samples were all residents aged 15-64 years of rural and urban areas in East Azerbaijan, Islamic Republic of Iran, who consented to participate (n = 990). The latest data available were collected from the Noncommunicable Disease Surveillance System of East Azerbaijan Province (2007). Data were analysed using SPSS version 19. Results: Based on multiple logistic regression, age, family history of T2DM and residence were the most important risk factors for T2DM. Based on ANN, age, body mass index and current smoking were most important. To test for generalization, ANN and logistic regression were evaluated using the area under the receiver operating characteristic curve (AUC). The AUC was 0.726 (SE = 0.025) and 0.717 (SE = 0.026) for logistic regression and ANN, respectively (P < 0.001). Conclusions: The logistic regression model is better than ANN and it is clinically more comprehensible.
Diabetes Mellitus Risk Prediction Using Artificial Neural Network
2019
Diabetes is a non-communicable disease and various types of dangerous diseases like heart attack, kidney failure, myopia, and so on are caused by it. The number of people suffering from diabetes is increasing rapidly. Though there has no perpetual cure for diabetes, it can be controlled by proper counseling and medication. For this perception, an early determination is needed. In our analysis, 464 patients data with 23 features were collected from various health-care units and preprocessed. A predictive model was developed with artificial neural network technique. Different learning rate, hidden layers were applied in our analysis. Average-weighted accuracy of all observations was approximately 99.69%.
Predicting Diabetes Mellitus and Analysing Risk-Factors Correlation
EAI Endorsed Transactions on Pervasive Health and Technology, 2020
INTRODUCTION: Diabetes mellitus is a common disease of the human body caused by a group of metabolic disorders where the sugar levels exceed a prolonged period, and that is very high than the usual time. It not only affects different organs of the human body but also harms a large number of the body system, in particular the blood veins and nerves. OBJECTIVES: Early predictions of this phenomenon can help us to control the disease and also to save human life. For achieving the goal, this research work mainly explores various risk factors such as kidney complications, blood pressure, hearing loss, and skin complications related to this disease using machine learning techniques and make a decision. METHODS: Machine learning techniques provide an efficient result to extract knowledge by constructing predicting models from diagnostic medical datasets collected from 200 diabetic patients from the Medical Centre Chittagong, Bangladesh using 16 attributes. Obtaining knowledge from such data can be useful to predict diabetes. In this work, we perform four popular machine learning algorithms, such as Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbour (KNN) and C4.5 Decision Tree (DT), on adult population dataset to predict Diabetes Mellitus. RESULTS: C4.5 Decision Tree performs better than other algorithms for predicting diabetes with 73.5% accuracy, 72% F-measure, and 0.69 of AUC (area under ROC curve). Besides, we determine the correlation between different risk factors of Diabetes Mellitus. The highest correlation is 0.81 for blood pressure (Hypertension) complications with diabetes. CONCLUSION: In this study, both positive and negative correlation has been established between the various risk factors and diabetes. There is a positive correlation for predicting kidney complications (Nephropathy) and blood pressure (Hypertension) complications and a negative correlation at predicting hearing loss and skin complications (diabetes dermopathy) from diabetic patients. It helps a patient to be aware of the risk factors related to diabetes.
A Neural Network based Approach for the Diabetes Risk Estimation
International Journal of Computer Applications, 2013
Diabetes is one of the most common anddramatically increasing metabolic diseases causes the increase in blood sugar. The patient having high blood sugar either caused by the bodyfailure to produce enough insulin (type 1) or the cells failure to respond to the produced insulin (type 2). Since the present medication cannot cure it hence the only way is to estimate the risk of diabetes for each person and take precautions according to the risk factor. This paper presents a Feed forward neural network based approach for the estimation of diabetes risk which estimates the risk factor for any person on the basis of body characteristics (like weight,Bloodpressure etc.).
Rangsi University, 2013
Among non-communicable diseases, diabetes kills the most people in Asia and is only becoming more prevalent in this region. Analyzing Type 2 diabetes risk factors utilizing prediction tools instead of blood testing is a challenge for accurate diabetes diagnosis. Recently, many researchers have studied the risk factors of diabetes by using Logistic Regression, Radial Basis and Back-propagation Neural Network (BNN) and applying them as a tool for diabetes prediction. This paper presents the new factors of smoking and alcohol consumption to improve performance in diabetes prediction. The predictive role of some traditional factors, i.e., body mass index, blood pressure and waist circumference and Family History (FMH) are also improved by adjusting the previously accepted ranges. The newly proposed diabetes prediction method is based on BNN. The sample data consists of 2,000 Thai people presenting at Bangkok hospital, Thailand from 2010 to 2012. From these experiments, it was found that an appropriate number of hidden nodes was equal to 50 nodes. Each proposed factor, i.e., FMH, alcohol consumption factor, smoking factor, and WC gave a better accuracy (correct in prediction) compared with a baseline model. Their accuracies were 83.35%, 83.50%, 83.60% and 83.65%, respectively. Subsequently, the new risk factor model performance was increased by tuning the neural network parameter learning rate. Our previously proposed factors for tuning BNN parameters introduced a high accuracy compared with the baseline model up to 1.2%. In this paper, the new proposed factors model introduces a better performance in Root Mean Square Error (RMSE) than the baseline factors model up to 25.75%, which are trained by the same sample data (2000 cases). Finally, the new model is implemented to be the diabetes prediction tool based on PHP web application, which works in conjunction with Matlab for predicting calculation. The threshold value returned is used to make a decision of whether or not patients have diabetes.
A Machine Learning Approach to Predicting Diabetes Complications
Healthcare, 2021
Diabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for building different models to predict and classify eight diabetes complications. The complications include metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy. For this study, a dataset collected by the Rashid Center for Diabetes and Research (RCDR) located in Ajman, UAE, was utilized. The dataset consists of 884 records with 79 features. Some essential preprocessing steps were applied to handle the missing values and unbalanced data problems. Furthermore, feature selection was performed to select the top five and ten features for each complication. The final number of records used to train and build the binary classifiers for each com...
Introduction: Diabetes is the most common endocrine disease caused by sugar, fat and protein metabolism disorder and is characterized by blood sugar increase. Pre-diabetic individuals are the most vulnerable people at risk of diabetes, therefore; in the present study pre-diabetic individuals are considered as control group versus diabetic patients. Depending on the nature of dependent and predictor variables, there are various statistical models that suit various situations to recognize and classify these characteristics. To achieve the above propose, this study analyzes efficiency and prediction power of three statistical models. Materials and Methods: Data are collected from 17 rural health centers in Kermanshah city. An experimental group of 100 diabetic and a control group of 100 pre-diabetic patients were entered into the study. The under study variables included demographic data, body mass index, fasting blood sugar, glucose tolerance, blood pressure, blood lipid and individuals' daily activity. The data were recorded in 2 separate checklists from the subjects' latest health record data obtained from Kermanshah rural health centers. Artificial Neural Network (ANN), logistic regression and discriminant analysis models were applied for data processing to identify risk factors. ROC curve was used to compare prediction powers of the models. To specify a model with the highest prediction, Radial Basis Function (RBF) and wrapper method were applied in ANN model. The method which took all the situations into consideration was applied to enter independent variables to the model; subsequently, a model with the highest prediction power was selected as ANN superior model. Results: According to area under the ROC curve, prediction power of the three models; RBF, logistic regression and discriminant analysis models were estimated as much as 0.864, 0.884 and 0.80, respectively. Gender variables (P=0.027) and fasting blood sugar (P<0.001) in Logistic regression model and age variables (P=0.014), fasting blood pressure (P<0.001) and glucose tolerance (P<0.001) in discriminant analysis model indicated significant correlation. According to wrapper method; the model consists of fasting blood sugar, glucose tolerance, BMI and activity with 82.1% prediction power turned out to be selected as the best RBF pattern (out of 242 possible models). RBF with 95.2% indicate the highest sensitivity among the three models. Conclusion: At the present study RBF had a higher level of accuracy and sensitivity although logistic regression performed more powerful to distinguish between diabetic and pre-diabetic patients. In communities with high affinity between experimental and control groups demand stronger methods to discover the differences between the groups. Therefore, application of these methods in medical studies is recommended.
Journal of Diabetes Research
Diabetes mellitus is a disease with no cure that can cause complications and even death. Moreover, over time, it will lead to chronic complications. Predictive models have been used to identify people with a tendency to develop diabetes mellitus. At the same time, there is limited information regarding the chronic complications of patients with diabetes. Our study is aimed at creating a machine-learning model that will be able to identify the risk factors of a diabetic patient developing chronic complications such as amputations, myocardial infarction, stroke, nephropathy, and retinopathy. The design is a national nested case-control study with 63,776 patients and 215 predictors with four years of data. Using an XGBoost model, the prediction of chronic complications has an AUC of 84%, and the model has identified the risk factors for chronic complications in patients with diabetes. According to the analysis, the most crucial risk factors based on SHAP values (Shapley additive explan...
Predictive Risk Analysis of Diabetes Using Machine Learning Approach
Diabetes can be considered as more frequently seen condition and it is found in different aged group of people which includes infants, children, and adults. In this paper we discuss about predictive risk analysis of diabetes in patients using step wise logistic regression with backward elimination process, Akaike Information Criterion (AIC) technique and other machine learning approaches which helps in Healthcare of patients in early stage.
Review of Predictive Analysis Techniques for Analysis Diabetes Risk
2019 Amity International Conference on Artificial Intelligence (AICAI)
Diabetes mellitus is one of the most commonplace persistent illnesses in nearly all nations, and keeps booming in numbers and importance, as economic development and urbanization result in changing lifestyles characterized by reduced physical hobby and expanded weight problems. In this paper we have reviewed various case related to diabetes mellitus based on different predictive analytics algorithm and we found that single algorithm is not sufficient for predictive analytics.