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

Authors

Amy Adyanthaya, Southern Methodist UniversityFollow
Dawn Bowerman, Southern Methodist UniversityFollow
Rachel Liercke, Southern Methodist UniversityFollow
Robert Slater, SMUFollow

Abstract

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.

Recommended Citation

Adyanthaya, Amy; Bowerman, Dawn; Liercke, Rachel; and Slater, Robert (2024) "Application for Prediction of Heart Failure; the Next Step in Machine Learning for Healthcare," SMU Data Science Review: Vol. 8: No. 3, Article 4.
Available at: https://scholar.smu.edu/datasciencereview/vol8/iss3/4

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License