An Advanced Machine Learning Framework for Multi-Label Classification of Heart Disease Complications

Heart disease continues to be the foremost cause of mortality across both developed and developing nations, posing a critical public health challenge. This life-threatening condition often necessitates urgent hospitalization and rapid diagnosis in emergency departments to avert fatal outcomes. Such outcomes can arise from a diverse range of complications associated with heart disease, including cardiogenic shock, pulmonary edema, myocardial rupture, the progression of congestive heart failure, thromboembolism, asystole, and ventricular fibrillation. Although numerous studies have focused on predicting heart disease, relatively few have addressed the complexity of multi-label classification for this issue. In this paper, we propose an innovative framework that integrates a K-Nearest Neighbors-based imputation technique with a Gradient Boosting algorithm to classify multiple heart disease complications. The experimental findings reveal that our approach achieves superior accuracy compared to existing methods. Additionally, we present a comprehensive comparative analysis of the proposed data pre-processing methodology by applying various machine learning algorithms. Moreover, we provide a detailed comparison between our multi-label classification strategy and previously established approaches. A diverse set of evaluation metrics, including accuracy, precision, recall, F1-score, and Hamming loss, is utilized to demonstrate the robustness and effectiveness of the proposed framework.