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In the emerging sector of medical applications, machine learning is critical in forecasting cardiovascular disease through the analysis of retinal pictures. The project’s focus on efficient data processing intends to improve the scalability of healthcare big data, reducing time consumption while increasing forecast accuracy. Diverse machine learning models are used to assess time complexity and accuracy, allowing for early diagnosis of risk factors connected with heart disease. The use of retinal imaging adds a new layer to cardiovascular disease diagnosis, combining big data and machine learning to produce artificial intelligence tools that directly assist clinicians. The project’s goal is to deliver high-performance and interpretable solutions for heart disease prediction through ongoing collaboration with healthcare professionals and adherence to ethical guidelines.

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Heart Disease Detection Using Fundus Imaging and Ml Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • H B Divyashree,
  • S Kokila,
  • Supraja Eduru,
  • Mythri Peddamariveedu

摘要

In the emerging sector of medical applications, machine learning is critical in forecasting cardiovascular disease through the analysis of retinal pictures. The project’s focus on efficient data processing intends to improve the scalability of healthcare big data, reducing time consumption while increasing forecast accuracy. Diverse machine learning models are used to assess time complexity and accuracy, allowing for early diagnosis of risk factors connected with heart disease. The use of retinal imaging adds a new layer to cardiovascular disease diagnosis, combining big data and machine learning to produce artificial intelligence tools that directly assist clinicians. The project’s goal is to deliver high-performance and interpretable solutions for heart disease prediction through ongoing collaboration with healthcare professionals and adherence to ethical guidelines.