ArticleScientific reports2025
Development and implementation of explainable AI-based machine learning models for predicting hospital stay and treatment costs in cardiovascular patients.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Cardiovascular disease (CVD) remains the leading cause of global morbidity and mortality, with substantial economic implications for healthcare systems. Among hospitalized CVD patients, procedures such as angioplasty and coronary artery bypass grafting (CABG) are associated with prolonged lengths of stay (LOS) and elevated treatment costs, underscoring the need for robust predictive tools to support clinical and administrative decision-making. Therefore, this study aimed to develop and validate machine learning (ML) models to predict hospital LOS and treatment costs for cardiovascular inpatients using real-world clinical data. This applied, retrospective predictive modeling study was conducted in 2024 at specialized cardiovascular clinic of a tertiary teaching hospital in Tehran, Iran. A cohort of 7685 adult inpatients who underwent angioplasty or CABG between 2022 and 2023 was analyzed. Eight regression-based ML algorithms were developed to predict four outcomes: hospital LOS, patient out-of-pocket (OOP), insurer payment, and total treatment cost. Model performance was evaluated using the coefficient of determination (R
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