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- Title
Classification of Type 2 Diabetes Mellitus Using Machine Learning in Sleman District of Yogyakarta Special Region, Indonesia.
- Authors
Nugroho, Dhite Bayu; Sinorita, Hemi; Pramono, Bowo; Ikhsan, Robikhul; Susanti, Vinayanti; Ar Rochmah, Mawaddah
- Abstract
Introduction: Diabetes mellitus type 2 (T2DM) is a chronic-multifactorial disease with a high disease burden. Identifying the risk factors for type 2 diabetes mellitus can help in designing prevention strategies. Surveillance system data can be utilized to accurately predict the prevalence of diseases in a community using machine learning algorithm. The aim of this study was to determine the performance of machine learning and to identify important features in classifying T2DM in the Health and Demographic Surveillance System (HDSS) population of the Sleman region of Yogyakarta, Indonesia. Methods: The first two cycles of the Sleman HDSS database were obtained, and factors such as demographics, risky foods, diet composition, and comorbidity were evaluated. After pre-processing the data, we employed binary classification of T2DM using logistic regression and the random forest method. The performance of the two models was then compared, and the imported features were reported. Results: There were 4,611 subjects included in this study including 463 with self-reported T2DM. Significant differences, such as age, level of education, monthly food expenditure, consumption of coffee or other caffeinated beverages, intake of herbs and instant noodles, and health issues, such as hypertension and stroke, were identified between the T2DM and non-DM groups. Apart from fat, rice is the most predominant food in all types of diet compositions. Conclusion: The random forest machine-learning algorithm shows superior performance, with hypertension being the most important feature in the classification of a self-reported T2DM in the Sleman HDSS population.
- Subjects
YOGYAKARTA (Indonesia); INDONESIA; TYPE 2 diabetes; MACHINE learning; SPECIAL districts; RANDOM forest algorithms; SOFT drinks
- Publication
Malaysian Journal of Medicine & Health Sciences, 2022, Vol 18, p89
- ISSN
1675-8544
- Publication type
Article