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Cardiovascular illness is the main concern of our research projects, a pandemic across the world and among leading causes of death. We are developing a modern, cutting-edge application which will enable diagnosing heart diseases using machine learning (ML), thereby creating transformative solutions for practitioners and laypersons. This paper presents an approach that uses supervised learning techniques to analyze certain characteristics tied with development of cardiac disorders. The proposed scheme employs a convolutional neural network method for predicting disease probabilities based on patient records that may be unstructured or partly structured. The correctness values obtained from the model were within 85% and 88%, reinforcing the dependability and soundness of our work. Based on these findings, we can put forward that this proposal has a firm foundation in effectiveness, thus raising confidence in our model’s potential to revolutionize heart disease diagnosis.

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Revolutionizing Heart Disease Diagnosis: A Novel Machine Learning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Foram Rangani,
  • Riya Kaku,
  • Deepak Kumar Verma,
  • Satvik Vats,
  • Hardik Doshi,
  • Manoj Kumar,
  • Monika Shah

摘要

Cardiovascular illness is the main concern of our research projects, a pandemic across the world and among leading causes of death. We are developing a modern, cutting-edge application which will enable diagnosing heart diseases using machine learning (ML), thereby creating transformative solutions for practitioners and laypersons. This paper presents an approach that uses supervised learning techniques to analyze certain characteristics tied with development of cardiac disorders. The proposed scheme employs a convolutional neural network method for predicting disease probabilities based on patient records that may be unstructured or partly structured. The correctness values obtained from the model were within 85% and 88%, reinforcing the dependability and soundness of our work. Based on these findings, we can put forward that this proposal has a firm foundation in effectiveness, thus raising confidence in our model’s potential to revolutionize heart disease diagnosis.