Machine Learning–Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study

• Published in: Journal of medical Internet research Vol. 27; no. 5; p. e58021
• Main Authors: Oh, Mi-Young, Kim, Hee-Soo, Jung, Young Mi, Lee, Hyung-Chul, Lee, Seung-Bo, Lee, Seung Mi
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 19.03.2025; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Aged; Automation; Computation; Creatinine; Disease; Female; Hospitals; Humans; Ischemia; Machine Learning; Male; Medical research; Medicine, Experimental; Middle Aged; Missing data; Mortality; Nonlinear Dynamics; Original Paper; Patients; Perioperative Period; Physiological aspects; Prediction models; Retrospective Studies; Risk factors; Stroke; Stroke (Disease); Stroke - diagnosis; Surgery; Variables; United States; effectiveness; efficiency; perioperative stroke; explainability; ML-based models; Nonlinear computation; real-world data; machine learning; computation scoring system; stroke; noncardiac; tool; noncardiac surgery; perioperative; score; application; patient; risk tool; risk; surgery
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/58021

Machine learning (ML) has the potential to enhance performance by capturing nonlinear interactions. However, ML-based models have some limitations in terms of interpretability. This study aimed to develop and validate a more comprehensible and efficient ML-based scoring system using SHapley Additive exPlanations (SHAP) values. 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. 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). 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.