IDENTIFYING HIGH-RISK CLUSTERS OF DIABETES IN WOMEN USING MACHINE LEARNING - Novia Hugo, Veronika; Agus Prana Dhiva Satvika, Putu; Ali, Raihan; Surya Putra Dilaga, Rahmat; Agus Rama Abdiyasa, Putu (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
This study focuses on identifying high-risk diabetes clusters in women using machine learning techniques. By applying the K-Means algorithm on diagnostic data from the Pima Indians Diabetes Database, the analysis categorizes patients into three distinct clusters based on health indicators such as glucose levels, BMI, age, and insulin levels. The Elbow Method determines the optimal number of clusters, revealing patterns that differentiate individuals based on their risk profiles. Results show that one cluster represents individuals with high diabetes risk due to elevated glucose and insulin levels, while another cluster indicates low-risk individuals with lower BMI and glucose levels. These findings highlight the potential of clustering for personalized diabetes care and intervention strategies. This research underscores the importance of integrating machine learning tools in public health to enhance the management of chronic diseases like diabetes.