Machine learning methods for diabetes prediction

Dzakiyullah, Nur Rachman and M.A., Burhanuddin and Raja Ikram, Raja Rina and Mohd Khanapi, Abd Ghani and Setyonugroho, Winny (2019) Machine learning methods for diabetes prediction. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8 (12). pp. 2199-2205. ISSN 2278-3075

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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

Item Type:	Article
Uncontrolled Keywords:	Machine Learning, Diabetes Prediction, Risk Factor, Accuracy
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	12 May 2022 14:35
Last Modified:	12 May 2022 14:35
URI:	http://eprints.utem.edu.my/id/eprint/24798
Statistic Details:	View Download Statistic

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