Diabetes diagnosis system using modified Naive Bayes classifier

In today’s world, Diabetes is one of these diseases and is now a big growing health problem. The techniques of data mining have been widely applied to extract knowledge from medical databases. In this work, a Medical Diagnosis system of Diabetes is proposed for the diagnosis of diabetes in a manner that is rapid and cost-effective. three stages are involved in the proposed diabetes diagnosis system (DDS) including: dataset constructing, preprocessing and classification algorithm using traditional Naïve Bayesian (TNB) and modified Naïve Bayesian (MNB)). MNB Classifier is a modified NB that is used to enhance the accuracy of diagnosis, by adding a proposed modest model to help separate the overlapping diagnosis classes. The outcome showed that the accuracy of MNB classifier is generally higher than that of TNB classifier for all sets of features. An accuracy of about (63%) was achieved for the TNB model, whereas that of the MNB model is (100%). The experimental results showed that the MNB is better than the traditional NB in both two cases of constructed medical datasets; the first case of filling the missing values by experiences and the second case of filling missing values by K-nearest neighbor (KNN) algorithm