Diabetes Diagnosis by Using Computational Intelligence Algorithms

Diabetes mellitus is a chronic disease and one of the most public health challenges in worldwide. Most of discoveries indicate that the best way to overcome diabetes is to prevent the risks of diabetes before becoming a diabetic. With this idea, we would like to find a way to estimate diabetes risk. Data mining techniques could be used as an alternative way in discovering knowledge from the patient medical records and they have shown remarkable success in the area of applying Computer Aided Diagnostic (CAD) systems. In this paper, we have applied several intelligence classifiers such as Bayesian, Functional, Rule-base, Decision Trees and Ensemble for diagnosing diabetes mellitus. Experimental results on Pima Indian Diabetes (PID) dataset show that Bagging ensemble classifier with Logistic core has better performance in comparison with other presented classifiers Keywords— Diabetes mellitus, Machine learning, Classifier, Pima Indian Diabetes (PID)