Abstract
The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers (PART and Decision table) to measure the accuracy and logistic regression on the classification results for forecasting the prevalence in diabetes mellitus patients suffering simultaneously from other chronic disease symptoms. The real-life data was collected in Nigeria between December 2017 and February 2019 by applying ten non-intrusive and easily available clinical variables. The results disclosed that the Rule classifiers achieved a mean accuracy of 98.75%. The error rate, precision, recall, F-measure, and Matthew’s correlation coefficient MCC were 0.02%, 0.98%, 0.98%, 0.98%, and 0.97%, respectively. The forecast decision, achieved by employing a set of 23 decision rules (DR), indicates that age, gender, glucose level, and body mass are fundamental reasons for diabetes, followed by work stress, diet, family diabetes history, physical exercise, and cardiovascular stroke history. The study validated that the proposed set of DR is practical for quick screening of diabetes mellitus patients at the initial stage without intrusive medical tests and was found to be effective in the initial diagnosis of diabetes.
Highlights
Diabetes mellitus (DM) is an exponentially growing disease across the developing countries of the 21st century
Proposed a mathematical model to forecast the prevalence of diabetes by using attributes of sex, age, risk factor status, and T2DM status and found T2DM prevalence is projected to increase by 43%, and the incidence is projected to increase 147% by 2050 in Qatar [10]
Each sample was utilized as validation data from the retention process, while the remaining nine samples served as the training data
Summary
Diabetes mellitus (DM) is an exponentially growing disease across the developing countries of the 21st century. Diabetes mellitus has become a worldwide challenge and identified as the risk factor of other chronic diseases such as hyperosmolar, diabetic ketoacidosis, and hyperglycemia and, in extreme cases, death. Proposed a mathematical model to forecast the prevalence of diabetes by using attributes of sex, age, risk factor status, and T2DM (type 2 diabetes mellitus) status and found T2DM prevalence is projected to increase by 43%, and the incidence is projected to increase 147% by 2050 in Qatar [10]. Applied support vector machine (SVM) and artificial neural network (ANN) to screen the pre-diabetes of 9251 individuals and performed a systematic assessment of the models using external and internal cross-validation and concluded that the results of the SVM method are better than the ANN [11]. Sohail et al performed the classification results on Weka by machine learning by utilizing the dataset of different diseases and concluded the accuracy ratio of the decision tree (86%), the Bayesian network (90%), the naïve
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