Literature Review for the Weighted Clustering and Deep Learning Methods for Diagnosing Heart Disease by 3D Imaging and Sophisticated Algorithms

Abstract

Cardiovascular disorders remain a major global health challenge, and rapid, accurate diagnosis is essential for improving therapeutic outcomes. The escalating volume and intricacy of clinical data demand advanced computational strategies. In particular, clustering methods that incorporate weighting schemes have shown great promise for handling large, imbalanced datasets, making them valuable tools for cardiac disease detection. Moreover, three-dimensional imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) deliver high resolution, volumetric views of the heart, facilitating the identification of subtle pathological changes. This article concentrates on the analysis of 3 D cardiac images and delivers an exhaustive review of the literature on weighted clustering and deep learning approaches applied to heart disease diagnosis. It also surveys emerging algorithms and hybrid frameworks that fuse clustering with deep learning to boost diagnostic precision and to discriminate among various cardiac conditions.