Evaluative Study of Machine Learning Classifiers in Predicting Heart Failure: A Focus on Imbalanced Datasets

Abstract

Heart disease persistently remains a paramount health concern globally, necessitating early and precise detection for effective therapeutic intervention, particularly within the realm of cardiology. This study proposes a predictive model for heart failure, utilizing six distinct machine learning classification algorithms-Stochastic Gradient Descent (SGD), Logistic Regression (LR), Decision Tree (DT), AdaBoost, Support Vector Machine (SVM), and Random Forest (RF)-and assesses their performance on an imbalanced heart failure clinical record dataset obtained from Kaggle. Consisting of 299 observations, the dataset comprises 32.11% of instances resulting in death and 67.89% marking recovery or survival, thereby presenting a significant imbalance. This imbalance potentially contributes to a suboptimal prediction of the non-death instances. To address this issue, the Synthetic Minority Oversampling Technique (SMOTE) is employed. The performance of each classifier is evaluated using measures such as accuracy, precision, recall, and F-score. Experiments are conducted on the complete feature set and a selected subset of features, focusing particularly on highly correlated features. The results from these experiments are then juxtaposed with those derived using the comprehensive feature set. The outcome of these comparative analyses reveals superior performance by the RF algorithm over other tree-based and statistical-based models, thereby achieving enhanced accuracy. This study, therefore, presents an in-depth evaluation of machine learning algorithms in predicting heart disease, contributing significantly to the ongoing research in cardiology and machine learning.