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The three leading causes of fatalities in today's world are heart disease, diabetes, and Parkinson's disease. This frequently results in persistent illnesses that have negative effects on quality of life. We have observed the usage of Machine Learning (ML) techniques in recent advancements across a variety of IoT domains. The methods we provided attempt to identify key traits by utilizing ML techniques, enhancing the accuracy of disease prediction. While we use both Logistic Regression and Support Vector Machine (SVM) algorithms for all three diseases, our results reveal that Logistic Regression yields a promising accuracy of 91.4% for heart disease, while SVM demonstrates accuracy levels of 96.2% for diabetes and 88.4% for Parkinson's disease. Additionally, we have developed an intuitive web application to facilitate user interaction. Our integrated prediction system enhances early disease identification for proactive healthcare management.

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Prediction of Chronic Diseases Using Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Murapaka Swathi,
  • R. Karthikeya,
  • K. Vishnu Vardhan,
  • K. Sai Pavan,
  • K. Prem Chand,
  • Telagarapu Prabhakar

摘要

The three leading causes of fatalities in today's world are heart disease, diabetes, and Parkinson's disease. This frequently results in persistent illnesses that have negative effects on quality of life. We have observed the usage of Machine Learning (ML) techniques in recent advancements across a variety of IoT domains. The methods we provided attempt to identify key traits by utilizing ML techniques, enhancing the accuracy of disease prediction. While we use both Logistic Regression and Support Vector Machine (SVM) algorithms for all three diseases, our results reveal that Logistic Regression yields a promising accuracy of 91.4% for heart disease, while SVM demonstrates accuracy levels of 96.2% for diabetes and 88.4% for Parkinson's disease. Additionally, we have developed an intuitive web application to facilitate user interaction. Our integrated prediction system enhances early disease identification for proactive healthcare management.