• Mrs.Swapnali Anirudha Limkar
• Mrs.Sarika Dattu Pawar

Heart disease remains a major global health con- cern, with early and accurate diagnosis playing a crucial role in improving treatment outcomes. The increasing complexity of medical datasets necessitates the use of advanced computa- tional techniques. Clustering algorithms, especially when enhanced with weighted strategies, are highly effective in handling large and imbalanced datasets, making them valuable in heart disease diag- nosis. Furthermore, 3D imaging methods, including CT and MRI scans, provide a more precise and detailed picture of the heart, which helps with the detection of certain cardiovascular diseases. With an emphasis on 3D image analysis, this paper offers a thorough assessment of the literature on the use of weighted clustering and deep learning techniques for the diagnosis of heart disease.The review also delves into advanced algorithms and hybrid methods that combine clustering and deep learning to improve diagnostic precision and identify various heart disease types.

Clustering algorithms, Weighted clustering, 3-D imaging, Diagnosis precision, Heart disease diagnosis

"Literature Review: Weighted Clustering and Deep Learning Approaches in Heart Disease Diagnosis Using 3D Imaging and Advanced Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.e62-e64, May-2025, Available :http://www.jetir.org/papers/JETIR2505474.pdf

"Literature Review: Weighted Clustering and Deep Learning Approaches in Heart Disease Diagnosis Using 3D Imaging and Advanced Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppe62-e64, May-2025, Available at : http://www.jetir.org/papers/JETIR2505474.pdf