Open AccessComparative Experiments for Classification of Diabetes Mellitus
Author(s) -
Siti Aida Fatimah Madon,
Aida Mustapha,
Mohd Zainuri Saringat,
Mohd Helmy Abd Wahab,
Syed Zulkarnain Syed Idrus
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/917/1/012042
Subject(s) - naive bayes classifier , diabetes mellitus , disease , bayes' theorem , computer science , feature selection , artificial intelligence , feature (linguistics) , medicine , health care , machine learning , data mining , bayesian probability , support vector machine , economic growth , pathology , linguistics , philosophy , economics , endocrinology
Diabetes is an affecting people disease nowadays. Over 246 million people worldwide with a majority of them being women had been affected by diabetes. According to the World Health Organization (WHO), by 2025, this number is expected to increase to over 380 million. The disease has been ranked as the fifth deadliest disease in United States with no imminent cure in sight. Along with the increasing of the information technology and its continued advent into the medical and healthcare sector, the cases of this disease and the symptoms are well documented. This paper aims at finding the best performance by four classification algorithms, which are Naive Bayes, Simple Logistics, REPTree, and Sequential Minimal Optimization (SMO). Model testing on 200 tuples with 9 attributes in the diabetes dataset revealed that Naive Bayes achieved highest accuracy of 85%. To improve the overall accuracy, it is necessary to use more data set with larger number of attributes and use a better feature selection method in future works.