Predicting Type 2 Diabetes: A Machine Learning Approach
Lim Zi Hao1, Mafas Raheem2, Seetha Letchumy3
1Lim Zi Hao, School of Computing, Asia Pacific University of Technology & Innovation, Kuala Lumpur, Malaysia.
2Mafas Raheem*, School of Computing, Asia Pacific University of Technology & Innovation, Kuala Lumpur, Malaysia.
3Seetha Letchumy, School of Computing, Asia Pacific University of Technology & Innovation, Kuala Lumpur, Malaysia.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 603-608 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3484079220/2020©BEIESP | DOI: 10.35940/ijrte.B3484.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diabetes is a well-known common disease among people around the world. Diabetes causes many anomalies in the body and results in the patients to become under a long term medication. Detecting diabetes has been done via hectic medical tests and causes a delay for the patients to get to know their test results. However, data mining and machine learning approaches are in the frontline supporting the health care domain to make effective predictions in this regard. This paper elaborates about predicting Type 2 Diabetes Mellitus using classification models. A suitable secondary dataset was used to build classification models and the more suitable model was selected via the valid performance measures. In this line, the Random Forest, Support Vector Machine, Naïve Bayes and Artificial Neural Network models were built. Based on the performance measures, Random Forest has been identified as the more suitable classifier with the accuracy of 90%, the recall and precision value of 0.90.
Keywords: Diabetes prediction, machine learning, predictive models, optimization, model tuning.