TY - JOUR AU - Jiang, Xiangkui AU - Wang, Bingquan PY - 2024 DA - 2024/12/31 TI - Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study JO - JMIR Med Inform SP - e58812 VL - 12 KW - prediction model KW - heart failure KW - hospital readmission KW - machine learning KW - cardiology KW - admissions KW - hospitalization AB - Background: Patients with heart failure frequently face the possibility of rehospitalization following an initial hospital stay, placing a significant burden on both patients and health care systems. Accurate predictive tools are crucial for guiding clinical decision-making and optimizing patient care. However, the effectiveness of existing models tailored specifically to the Chinese population is still limited. Objective: This study aimed to formulate a predictive model for assessing the likelihood of readmission among patients diagnosed with heart failure. Methods: In this study, we analyzed data from 1948 patients with heart failure in a hospital in Sichuan Province between 2016 and 2019. By applying 3 variable selection strategies, 29 relevant variables were identified. Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. Results: The graph convolutional network model showed the highest prediction accuracy with an area under the receiver operating characteristic curve of 0.831, accuracy of 75%, sensitivity of 52.12%, and specificity of 90.25%. Conclusions: The model crafted in this study proves its effectiveness in forecasting the likelihood of readmission among patients with heart failure, thus serving as a crucial reference for clinical decision-making. SN - 2291-9694 UR - https://medinform.jmir.org/2024/1/e58812 UR - https://doi.org/10.2196/58812 DO - 10.2196/58812 ID - info:doi/10.2196/58812 ER -