Classification Algorithm in Predicting the Diabetes in Early Stages

Abstract

Diabetes is a standout amongst the deadliest and Chronical diseases which can increase the blood sugar in the human body. Diabetes gives several complications if it is not diagnosed and treated where it might lead to lifeless. Diabetes could be actively controlled when it is primary predicted. To solve this problem and to predict the diabetes in early stage, the machine learning process is used. In this research work, the classifiers like Naive Bayes, KSTAR, ZeroR, OneR, J48 and Random Forest are implemented to predict the diabetes at primary point. Diabetes dataset is sourced from UCI repository and used for this study. The results are evaluated against the performance, accuracy and time. This research work shows the Naïve Bayes classification algorithms is the best in predicting the diabetes diseases in primary stage where it helps the health professional to start in diagnosing the patient for diabetes and to save the patient life.