Prediction Models for Diabetes Mellitus Incidence
Автор: Awoyelu I. O., Ojewande A. O., Kolawole B. A., Awoyelu T. M.
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 4 Vol. 12, 2020 года.
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Diabetes mellitus is an incurable disease with global prevalence and exponentially increasing incidence. It is one of the greatest health hazards of the twenty-first century which poses a great economic threat on many nations. The premise behind effective disease management in healthcare system is to ensure coordinated intervention targeted towards reducing the incidence of such disease. This paper presents an approach to reducing the incidence of diabetes by predicting the risk of diabetes in patients. Diabetes mellitus risk prediction model was developed using supervised machine learning algorithms of Naïve Bayes, Support Vector Machine and J48 Decision Tree. The decision tree was able to give a prediction accuracy of 95.09% using rules of prediction that give acceptable results, that is, the model was approximately 95% accurate. The easy-to-understand rules of prediction got from J48 decision tree make it excellent in developing predictive models.
Diabetes mellitus, supervised machine learning, feature extraction, prediction
Короткий адрес: https://sciup.org/15017459
IDR: 15017459 | DOI: 10.5815/ijitcs.2020.04.04
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