Application of data mining methods in diabetes prediction

Abstract

Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is help to make predictions on medical data. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. The methods strongly based on the data mining techniques can be effectively applied for high blood pressure risk prediction. In this paper, we explore the early prediction of diabetes via five different data mining methods including: GMM, SVM, Logistic regression, ELM, ANN. The experiment result proves that ANN (Artificial Neural Network) provides the highest accuracy than other techniques.