Geographic Analysis of Risk Factors for Chronic Respiratory Non-Communicable Diseases using Machine Learning

Chronic respiratory diseases (CRDs) are a significant category of non-communicable diseases (NCDs), affecting 235 million asthma patients and 64 million COPD patients globally.In Central Java, CRDs accounted for 6% of total deaths in 2023, with WHO projecting COPD as the third leading cause of death by 2030.This study employs a decision tree machine learning approach to analyze lifestyle behaviors and environmental factors, aiming to identify key CRD risk factors and map their geographic distribution.Results indicate public transportation contributes most significantly (0.3410), followed by smoking habits and NO concentration.The model achieved an RMSE of 0.40 and R of 0.83, reflecting high predictive accuracy.This approach provides insights and enhances healthcare access in high-risk areas.