Mathematical Insights and Applications of K-Means Clustering in Diabetes Data Analysis

Abstract

K-Means clustering is a powerful and adaptable technology that has transformed industries like operations research and healthcare due to the quick development of machine learning and data analytics. Using K-Means, this study examines a real- world dataset of 1050 diabetic patients that has been divided into groups based on important clinical criteria such as HbA1c levels, random blood sugar (RBS), post- prandial blood sugar (PPBS), and fasting blood sugar (FBS). Utilizing operations research methods, our goals are to pinpoint unique patient groups, improve individualized care plans, and maximize the use of available resources. The effectiveness of K-Means clustering in diabetes management is demonstrated in this study, which also provides important insights for individualized healthcare and shows the algorithm’s superiority over other machine learning techniques.