Predicting Hospital Readmission Risk for Heart Failure Patients Using Machine Learning Techniques: A Comparative Study of Classification Algorithms
Venkata Raghuveer Burugadda, Prashant S. Pawar, Abhishek Kumar, Neha Bhati
Abstract
Heart failure is a frequent cause of hospitalization and readmission because of the severity of the disease. Researchers explored using Machine Learning (ML) algorithms to forecast whether heart failure patients must be readmitted to the hospital. This study examines ML algorithms that use data from electronic health records to forecast hospital readmissions for patients with heart failure. We will assess the accuracy, precision, recall, and F1-score of logistic regression, decision trees, random forests, Support Vector Machines (SVM), and artificial neural networks. The study's results will show how well ML algorithms predict heart failure patients' hospital readmission risk, which could lead to personalized therapies that improve patient outcomes and save healthcare costs. Comparing studies in this field shows gaps in model interpretability, generalizability, and socioeconomic determinants of health in prediction models.