Interpretable SHAP-based machine learning framework for patient satisfaction prediction: a case study in Thammasat University Hospital

BMC Medical Informatics and Decision Making

卷:26

Research

Tanatorn Tanantong¹  Warut Pannakkong²  Nittaya Chemkomnerd¹  Prachya Boonkwan³ 

[1] School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, 12120, Pathum Thani, Thailand

[2] Thammasat University Research Unit in Data Innovation and Artificial Intelligence, Department of Computer Science, Faculty of Science and Technology, Thammasat University, 12120, Pathum Thani, Thailand

[3] School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, 12120, Pathum Thani, Thailand

关键词: Patient satisfaction;    Explainable artificial intelligence;    Machine learning;    Ophthalmology outpatient department;    Decision support systems;   

DOI  :  10.1186/s12911-026-03647-2

 received in 2026-02-17, accepted in 2026-06-17,  发布年份 2026

来源: Springer

【 摘 要 】

BackgroundPatient satisfaction is a critical indicator of healthcare service quality, particularly in specialized outpatient departments where service processes are complex and resources are constrained. This study aims to develop an explainable artificial intelligence framework to predict patient satisfaction and to identify the key factors influencing satisfaction in a public ophthalmology outpatient department.MethodsSurvey data were collected and patient satisfaction was transformed into both binary and three-class settings. Multiple machine learning models, including Random Forest, Gradient Boosting, Extreme Gradient Boosting, Categorical Boosting, and Light Gradient Boosting, were evaluated using a nested stratified cross-validation with 5 outer folds and 5 inner GridSearchCV folds. To address class imbalance, SMOTE-NC and G-SMOTENC were applied only within the training folds. Model performance was assessed to identify the best-performing predictive approach, which was subsequently analyzed using SHapley Additive exPlanations (SHAP).ResultsAmong the evaluated models, Gradient Boosting with G-SMOTENC achieved the highest performance in the binary setting, while Random Forest performed best in the three-class setting. SHAP analysis showed that length of stay contributed most strongly to satisfaction predictions in both settings, followed by demographic and visit-related variables.ConclusionThe results suggest that combining predictive modeling with SHAP-based interpretation can support transparent, context-specific assessment of patient satisfaction in the studied outpatient setting. The proposed approach provides interpretable, context-specific insights that may support data-driven resource management and future service improvement in specialized outpatient settings.

【 授权许可】

CC BY-NC-ND   
© The Author(s) 2026

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