Prediction Models for Diabetes Mellitus Incidence

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Awoyelu I. O. 1,* Ojewande A. O. 1 Kolawole B. A. 2 Awoyelu T. M. 1

1. Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

2. Department of Medicine, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria

* Corresponding author.


Received: 20 Dec. 2019 / Revised: 27 Dec. 2019 / Accepted: 1 Jan. 2020 / Published: 8 Aug. 2020

Index Terms

Diabetes mellitus, supervised machine learning, feature extraction, prediction


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.

Cite This Paper

K.Karpagam, Awoyelu I. O., Ojewande A. O., Kolawole B. A., Awoyelu T. M., "Prediction Models for Diabetes Mellitus Incidence", International Journal of Information Technology and Computer Science(IJITCS), Vol.12, No.4, pp.28-37, 2020. DOI:10.5815/ijitcs.2020.04.04


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