An ensemble approach for detection of diabetes using SVM and DT

Abstract

As diabetes affects the health of the entire population, it is a chronic disease that is still an important worldwide health issue. Diabetes increases the possibility of long-term complications, such as kidney failure and heart disease. If this disease is discovered early, people may live longer and in better health. In order to detect and prevent particular diseases, machine learning (ML) has become essential. An ensemble approach for detection of diabetes using support vector machine (SVM) and decision tree (DT) presents in this paper. In this case, to identify diabetes, two ML techniques are DT and SVM have been combined with an ensemble classifier. They obtain the information, they require from the Public Health Institute’s statistics area. There are 270 records, or instances, in the collection. This dataset includes the following attributes: age, a body mass index (BMI) glucose, and insulin. The development of a system that predictions a patient’s risk of diabetes is the goal of this analysis. Several performance metrics, including F1-score, recall, accuracy, and precision, were used to achieve this. From overall results, 96% of precision, 97% of accuracy, 96% of F1-score, and 97% of recall values are the results achieved for the ensemble model (SVM+DT) which is more effective than other individual ML models as DT and SVM.