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The concept of using machines to learn about diseases has been a development in health care since it can diagnose disease with high accurate and at the right time. The ability to simultaneously predict several diseases, including “heart diseases, diabetes, Parkinson’s diseases, and liver diseases” will result in early diagnoses and treatment thereby improving patients’ survival rates and decreasing the overall spending on health care. Predominantly, this paper is concerned with the use of machine learning in the modeling and prognosis of these diseases and prospects, limitations, and feasibility of this technique. This section describes a list of algorithms usually employed in classifying diseases and a list of algorithms usually employed in classifying diseases and a list of sources of data that is used in evaluating the diseases. Thus, the need to perform feature selection, model evaluation, and the fusion of the multimodal data for improved accuracy of the predictions is demonstrated. The finding of this work can substantiate the contribution of machine learning into the prediction of multiple diseases and the emerging degree, indicating its impact on the population’s health.

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Comprehensive Multiple Diseases Prediction Using Advanced Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • N. R. Sathis Kumar,
  • S. Venkata Ramana Reddy,
  • S. Venkata Mahendra,
  • S. Venkata Ravindra Reddy,
  • V. Keshavardhan Reddy

摘要

The concept of using machines to learn about diseases has been a development in health care since it can diagnose disease with high accurate and at the right time. The ability to simultaneously predict several diseases, including “heart diseases, diabetes, Parkinson’s diseases, and liver diseases” will result in early diagnoses and treatment thereby improving patients’ survival rates and decreasing the overall spending on health care. Predominantly, this paper is concerned with the use of machine learning in the modeling and prognosis of these diseases and prospects, limitations, and feasibility of this technique. This section describes a list of algorithms usually employed in classifying diseases and a list of algorithms usually employed in classifying diseases and a list of sources of data that is used in evaluating the diseases. Thus, the need to perform feature selection, model evaluation, and the fusion of the multimodal data for improved accuracy of the predictions is demonstrated. The finding of this work can substantiate the contribution of machine learning into the prediction of multiple diseases and the emerging degree, indicating its impact on the population’s health.