Machine Learning Methods for Diabetes Prediction
Nur Rachman Dzakiyullah1, M.A. Burhanuddin2, Raja Rina Raja Ikram3, Khanapi Abdul Ghani4, Winny Setyonugroho5
1Nur Rachman Dzakiyullah, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia and Faculty of Science and Technology, Department of Information Technology, Universitas ‗Aisyiyah Yogyakarta (UNISA), Indonesia.
2M.A. Burhanuddin, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia.
3Raja Rina Raja Ikram, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia.
4Khanapi Abdul Ghani, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia.
5Winny Setyonugroho, Faculty of Medicine and Health Science Muhammadiyah University of Yogyakarta (UMY), Yogyakarta, Indonesia.
Manuscript received on September 19, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2199-2205 | Volume-8 Issue-12, October 2019. | Retrieval Number: L29731081219/2019©BEIESP | DOI: 10.35940/ijitee.L2973.1081219
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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: Machine Learning is one of the methods used for task prediction. In the diabetic’s research field, the application of machine learning is emerging since the advantages of approximation on the prediction technique has significantly given insight for many health practitioners. Machine Learning is utilized in order to handle the uncontrollable risk factor by finding a relation between such a risk factor trough prediction. This study aims to review recent machine learning models that have been used in diabetes prediction with respect to the risk factors in order to prevent diabetes. This study compares the performance of the model by justified the accuracy as the baseline to evaluate the model. The result of this review shows that the Random Forest and Support Vector Machine are the most popular technique among researcher. Moreover, from this study, it can be seen that Type 2 Diabetes Mellitus (T2DM) has been a concern by researchers since the incidence of diabetes was increasing in worldwide today that happened from an uncontrollable risk factor.
Keywords: Machine Learning, Diabetes Prediction, Risk Factor, Accuracy.
Scope of the Article: Machine Learning