Machine learning prediction model for medical environment comfort based on SHAP and LIME interpretability analysis

Abstract

Medical environment comfort directly affects patient treatment outcomes and recovery processes. This study constructs a machine learning prediction model for patient excessive discomfort based on environmental monitoring data from medical infusion rooms. The research collected 1,000 samples with 11 environmental feature data, including temperature, humidity, noise level, air quality index, wind speed, lighting intensity, oxygen concentration, carbon dioxide concentration, air pressure, air circulation speed, and air pollutant concentration. Through comparative analysis of 10 machine learning algorithms, XGBoost model demonstrated the best performance with accuracy of 85.2%, precision of 86.5%, recall of 92.3%, F1-score of 0.893, and ROC-AUC of 0.889. Using SHAP and LIME interpretability methods, analysis revealed that air quality index (importance score 1.117) and temperature (importance score 1.065) are the most critical factors affecting patient comfort, followed by noise level (0.676) and humidity (0.454). SHAP partial dependence analysis revealed specific impact patterns of environmental factors: humidity shows positive correlation with discomfort, noise level exhibits strong linear positive correlation, temperature demonstrates nonlinear relationships, and air quality deterioration significantly increases patient discomfort. LIME local explanations validated the consistency of analysis results, providing scientific basis for personalized environmental control. The research results indicate that machine learning methods based on multi-sensor environmental monitoring can effectively predict patient discomfort. Interpretability analysis reveals the influence mechanisms of environmental factors, providing important support for intelligent management of medical environments and formulation of scientific control strategies.