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This study investigates cutting-edge approaches for heart disease detection, emphasizing advanced machine learning techniques and their role in cardiac imagingBetter patient outcomes depend on an early and accurate diagnosis because heart disease remains one of leading causes of mortality globally. The evaluation of various methodologies, particularly Improved Support Vector Machine (SVM) techniques, which have demonstrated notable potential in enhancing diagnostic accuracy. Our comparison of these methods against traditional approaches reveals the superior effectiveness of Improved SVM, achieving an accuracy rate of 96.1% in identifying cardiac anomalies. Additionally, we explore optimization techniques to further enhance these algorithms, addressing current limitations and boosting prognostic capabilities. In order to highlight the need for continuous innovation in this sector, this research attempts to provide insight into the efficacy of various approaches by conducting a complete analysis of the current state of heart disease diagnosis. The findings indicate that combining optimization strategies with machine learning models yield more reliable and efficient diagnostic tools. Ultimately, this research supports efforts to improve heart disease management and prevention, aiming to significantly reduce the global burden of cardiovascular conditions.

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Exploring State-of-the-Art Approaches for Heart Disease Detection: A Detailed Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • B. Shamna,
  • C. P. Maheswaran

摘要

This study investigates cutting-edge approaches for heart disease detection, emphasizing advanced machine learning techniques and their role in cardiac imagingBetter patient outcomes depend on an early and accurate diagnosis because heart disease remains one of leading causes of mortality globally. The evaluation of various methodologies, particularly Improved Support Vector Machine (SVM) techniques, which have demonstrated notable potential in enhancing diagnostic accuracy. Our comparison of these methods against traditional approaches reveals the superior effectiveness of Improved SVM, achieving an accuracy rate of 96.1% in identifying cardiac anomalies. Additionally, we explore optimization techniques to further enhance these algorithms, addressing current limitations and boosting prognostic capabilities. In order to highlight the need for continuous innovation in this sector, this research attempts to provide insight into the efficacy of various approaches by conducting a complete analysis of the current state of heart disease diagnosis. The findings indicate that combining optimization strategies with machine learning models yield more reliable and efficient diagnostic tools. Ultimately, this research supports efforts to improve heart disease management and prevention, aiming to significantly reduce the global burden of cardiovascular conditions.