Scientific Reports· 2025Q1
Machine learning prediction model for medical environment comfort based on SHAP and LIME interpretability analysis
- 32citations
- Q1SCImago
- 2025year
Short summary
An XGBoost model accurately predicts patient discomfort in medical infusion rooms (85.2% accuracy), identifying air quality index and temperature as the most critical factors.
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Key points
- XGBoost model predicts patient discomfort with 85.2% accuracy, 86.5% precision, and 92.3% recall.
- Air quality index (importance 1.117) and temperature (importance 1.065) are the most significant factors influencing patient comfort.
- SHAP analysis shows humidity positively correlates with discomfort, noise has a strong linear positive correlation, and temperature has nonlinear relationships.
- Interpretability methods (SHAP, LIME) provide insights into the influence mechanisms of environmental factors on patient comfort.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Scientific Reports, 2025 · DOI ↗
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Field: Health Information Management
Health Information ManagementHealth Professions