Identifying Influential Predictors of Loneliness in a Thai Community: A Cross-Sectional Machine Learning Analysis

This research addresses the increasing public health concern of loneliness within the Thai community by developing and validating a Machine Learning (ML) model for risk detection. The objective is to use ML to identify the most influential psychological, physical, and socioeconomic factors contributing to loneliness, providing insights that extend beyond standard self-assessments. A cross-sectional, multi-platform data collection approach was utilized, employing the LINE application (the "Senior Thailand" Official Account) and the "senior.in.th" website to gather comprehensive information from 616 adults across various provinces in Thailand. The methodology included all 12 factors for predictive modeling, encompassing sociodemographic traits, lifestyle habits (e.g., exercise and Activities of Daily Living), physiological metrics (e.g., BMI and blood pressure), and detailed mental health assessments (e.g., UCLA Loneliness Scale and Depression/Anxiety scales). Four algorithms Decision Tree, Random Forest, Logis