Machine learning models for predicting medium-term heart failure prognosis: Discrimination and calibration analyses

The number of patients with heart failure (HF) is increasing with the aging population, shifting care from hospitals to clinics. Although predicting medium-term prognosis after discharge can enhance care and reduce readmissions, yet no established model has been evaluated for both discrimination and calibration. This multicenter study developed and validated machine learning (ML) models—including logistic regression, random forests, extreme gradient boosting, and light gradient boosting— to predict 180-day mortality or emergency hospitalization in 4,904 HF patients with HF. Patients were randomly split into training and validation sets (8:2), and models were trained and evaluated accordingly. All models showed acceptable performance based on the area under the precision-recall curve, good calibration according to the calibration slope and Brier score, and effective risk stratification. The SHapley Additive exPlanations algorithm identified nursing care needs as a key predictor alongside established laboratory values for HF prognosis. ML models effectively predict the 180-day prognosis patients with HF, with nursing care needs highlighting the importance of multidisciplinary collaboration. Clinical Trial Registration : URL: https://www.umin.ac.jp/ctr; unique identifier: UMIN000054854