Application for Prediction of Heart Failure; the Next Step in Machine Learning for Healthcare

Heart failure (HF) is a serious medical condition affecting approximately 6.7 million U.S. adults and is expected to impact 8.5 million Americans by 2030 [1]. Heart failure is a complicated clinical ailment and characterizes the final course of numerous heart diseases [2]. This paper introduces a machine-learning-based application that utilizes Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and XGBoost models, implemented through the Python Flask framework, to predict HF risk using clinical data. The results indicate high model performance, with precision and recall metrics underscoring the application’s reliability in identifying at-risk patients. By providing real-time, accessible insights, this tool aims to enhance diagnostic accuracy and support early intervention, withparticular value for underserved populations. The study also discusses limitations related to data access, privacy concerns, and model generalizability, recommending future research to address these challenges for broader clinical adoption.