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<p>Soft computing is extremely useful in the field of medical science and extensive work is being done to build software applications based on a variety of soft computing techniques. As per various international reports, cardiac failure is one of the most prominent cause of fatality in humans. It has been medically proven that mortality rate from cardiac failures can be significantly reduced if heart diseases are detected early and proper cure is provided in a patient’s life. This survey is an analysis of various research work done in the field of heart disease detection using soft computing models based on fuzzy logic, neural networks, genetic algorithms, evolutionary algorithms and their hybrid combinations. Besides highlighting challenges, this study also highlights and offers new research opportunities for researchers and academicians in this field. This paper proposes a highly automated, low cost and time-saving cardiovascular disease diagnostic model using fuzzy logic system optimized by particle swarm optimization. The hybrid system achieves an accuracy of 95.83% along with specificity and sensitivity of 92.98% and 98.18% respectively. The other statistical parameters like F-score, Precision, AUC, TPR and FPR add strength to the research paper. Further, the paper motivates research enthusiasts to continue exploration and validation of their approach to improvise the soft computing techniques by analyzing and comparing the algorithms, models, input parameters, outcomes and accuracy of the past work done by the fellow research scholars.</p>

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A State of the Art Survey of Soft Computing Based Approaches for Heart Disease Detection and Development of a Novel PSO-Fuzzy Approach

  • 期刊论文

  • 发表日期：

    2025年10月11日

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  • Jagmohan Kaur,
  • Baljit S. Khehra,
  • Amarinder Singh

摘要

Soft computing is extremely useful in the field of medical science and extensive work is being done to build software applications based on a variety of soft computing techniques. As per various international reports, cardiac failure is one of the most prominent cause of fatality in humans. It has been medically proven that mortality rate from cardiac failures can be significantly reduced if heart diseases are detected early and proper cure is provided in a patient’s life. This survey is an analysis of various research work done in the field of heart disease detection using soft computing models based on fuzzy logic, neural networks, genetic algorithms, evolutionary algorithms and their hybrid combinations. Besides highlighting challenges, this study also highlights and offers new research opportunities for researchers and academicians in this field. This paper proposes a highly automated, low cost and time-saving cardiovascular disease diagnostic model using fuzzy logic system optimized by particle swarm optimization. The hybrid system achieves an accuracy of 95.83% along with specificity and sensitivity of 92.98% and 98.18% respectively. The other statistical parameters like F-score, Precision, AUC, TPR and FPR add strength to the research paper. Further, the paper motivates research enthusiasts to continue exploration and validation of their approach to improvise the soft computing techniques by analyzing and comparing the algorithms, models, input parameters, outcomes and accuracy of the past work done by the fellow research scholars.