Using Machine Learning to Predict Heart Failure: A Comparative Analysis of Various Classification Algorithms

Abstract

This study presents a comprehensive analysis of machine learning algorithms for predicting heart failure, a significant cause of morbidity and mortality worldwide. Employing a robust dataset of patient records with diverse clinical features, the performance of several widely-used classification algorithms, including K-Nearest Neighbours (KNN), Decision Trees, Support Vector Machines (SVM), Random Forests (RF), and Gaussian Naive Bayes etc. are systematically evaluated and compared. The methodology encompasses loading the dataset, data pre-processing, feature selection, model training, and validation. The performance of each algorithm is rigorously assessed using metrics such as accuracy, precision, recall, F1-score, and the area under the ROC curve with Random Forest outperforming the other classification algorithms. The study further investigates the influence of hyper-parameter tuning and cross-validation on model efficacy. Additionally, an interpretative analysis of the models, offering insights into feature importance and the clinical relevance of the prediction outcomes were provided. The findings aim to contribute to the field of medical informatics by identifying the most effective machine learning strategies for heart failure prediction, thereby facilitating early intervention and personalized patient care. This research not only underscores the potential of machine learning in healthcare but also highlights the challenges and considerations in developing reliable predictive models for complex medical conditions.