Population Segmentation and Recommendations Based on Disease Risk and Preventive Awareness

Abstract

Chronic non-communicable diseases represent a major challenge in the global healthcare landscape. To effectively prevent and manage these diseases, understanding the disease risk and awareness of preventive measures among community residents is of paramount importance. This study collected questionnaire data and conducted comprehensive analysis. This paper’s objective was to categorize residents based on multidimensional factors and provide personalized health recommendations. This paper achieved this by establishing a three-dimensional mapping model and employing the K-Medoids clustering algorithm to classify samples into four categories. Based on these categories, the paper offered diverse health advice encompassing dietary adjustments, exercise habits, regular check-ups, smoking cessation, and alcohol restriction. These recommendations aim to reduce disease risk and effectively manage chronic non-communicable diseases.