Analysis and Prediction of Heart Failure Using Machine Learning Algorithms

Inadequate management of heart failure (HF), a critical medical illness, can result in significant morbidity and mortality. Machine learning can help with early HF detection, which is essential for better patient outcomes (ML). classification algorithms have shown great potential in this regard. In this study, we give a full evaluation of diverse ML classification models for HF prediction. The study involves pre-processing of a large, publicly available dataset of demographic, clinical, and physiological variables. We then compare the performance of different algorithms, incorporating support vector machines, decision trees, random forests, and logistic regression, among others. Our results indicate that the Gradient boosting algorithm provides the most effective in terms of accuracy with 87%, F1 score of 87.2% and recall of 87.1 %. The study's conclusions offer insightful information about how to use ML algorithms for HF prediction and highlight the potential for improvement in the early detection and management of this disease.