TY  - JOUR
AU  - Oh, Mi-Young
AU  - Kim, Hee-Soo
AU  - Jung, Young Mi
AU  - Lee, Hyung-Chul
AU  - Lee, Seung-Bo
AU  - Lee, Seung Mi
PY  - 2025
DA  - 2025/3/19
TI  - Machine Learning–Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study
JO  - J Med Internet Res
SP  - e58021
VL  - 27
KW  - machine learning
KW  - explainability
KW  - score
KW  - computation scoring system
KW  - Nonlinear computation
KW  - application
KW  - perioperative stroke
KW  - perioperative
KW  - stroke
KW  - efficiency
KW  - ML-based models
KW  - patient
KW  - noncardiac surgery
KW  - noncardiac
KW  - surgery
KW  - effectiveness
KW  - risk tool
KW  - risk
KW  - tool
KW  - real-world data
AB  - Background: Machine learning (ML) has the potential to enhance performance by capturing nonlinear interactions. However, ML-based models have some limitations in terms of interpretability. Objective: This study aimed to develop and validate a more comprehensible and efficient ML-based scoring system using SHapley Additive exPlanations (SHAP) values. Methods: We developed and validated the Explainable Automated nonlinear Computation scoring system for Health (EACH) framework score. We developed a CatBoost-based prediction model, identified key features, and automatically detected the top 5 steepest slope change points based on SHAP plots. Subsequently, we developed a scoring system (EACH) and normalized the score. Finally, the EACH score was used to predict perioperative stroke. We developed the EACH score using data from the Seoul National University Hospital cohort and validated it using data from the Boramae Medical Center, which was geographically and temporally different from the development set. Results: When applied for perioperative stroke prediction among 38,737 patients undergoing noncardiac surgery, the EACH score achieved an area under the curve (AUC) of 0.829 (95% CI 0.753-0.892). In the external validation, the EACH score demonstrated superior predictive performance with an AUC of 0.784 (95% CI 0.694-0.871) compared with a traditional score (AUC=0.528, 95% CI 0.457-0.619) and another ML-based scoring generator (AUC=0.564, 95% CI 0.516-0.612). Conclusions: The EACH score is a more precise, explainable ML-based risk tool, proven effective in real-world data. The EACH score outperformed traditional scoring system and other prediction models based on different ML techniques in predicting perioperative stroke. 
SN  - 1438-8871
UR  - https://www.jmir.org/2025/1/e58021
UR  - https://doi.org/10.2196/58021
DO  - 10.2196/58021
ID  - info:doi/10.2196/58021
ER  -