Early detection system of risk factors for diabetes mellitus type 2 utilization of machine learning-random forest

Abstract

Abstract BACKGROUND: The prevalence of morbidity and mortality for type 2 diabetes mellitus (DM) is still increasing because of changing lifestyles. There needs to be a means of controlling the rise in the incidence of the disease. Many researchers have utilized technological advances such as machine learning for disease prevention and control, especially in noncommunicable conditions. Researchers are, therefore, interested in creating an early detection system for risk factors of type 2 diabetes. MATERIALS AND METHODS: The study was conducted in February 2022, utilizing secondary surveillance data from Puskesmas Johar Baru, Jakarta, in 2019, 2020, and 2021. Data was analyzed utilizing various bivariate and multivariate statistical methods at 5% significance level and machine learning methods (random forest algorithm) with an accuracy rate of >80%. The data for the three years was cleaned, normalized, and merged. RESULTS: The final population was 65,533 visits out of the initial data of 196,949, and the final number of DM 2 population was 2766 out of the initial data of 9903. Age, gender, family history of DM, family history of hypertension, hypertension, high blood sugar levels, obesity, and central obesity were significantly associated with type 2 DM. Family history was the strongest risk factor of all independent variables, odds ratio of 15.101. The classification results of feature importance, with an accuracy rate of 84%, obtained in order were age, blood sugar level, and body mass index. CONCLUSION: Blood sugar level is the most influential factor in the incidence of DM in Puskesmas Johar Baru. In other words, a person with a family history of type 2 diabetes, at unproductive age, of female gender, and of excessive weight can avoid type 2 diabetes if they can regularly maintain their blood sugar levels.