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Cardiovascular Disease Long-Term Care Risk Prediction by Claims Data Analysis Using Machine Learning

Author

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  • Sourabh Pawar
  • Tejas Pawar
  • Pranav More
  • Priti Rathod

Abstract

Heart disease is a major global health concern, especially in predicting cardiovascular issues. Machine learning (ML) and the Internet of Things (IoT) offer new ways to analyze healthcare data. However, current research lacks depth in using ML for heart disease prediction. To fill this gap, we propose a unique method that uses ML to identify key features for better heart disease prediction accuracy. Our model combines various features and classification techniques to achieve an accuracy of 88.7% in predicting heart disease, with the hybrid random forest and linear model (HRFLM) proving particularly effective. This study advances heart disease detection by integrating ML and IoT technologies.

Suggested Citation

  • Sourabh Pawar & Tejas Pawar & Pranav More & Priti Rathod, 2024. "Cardiovascular Disease Long-Term Care Risk Prediction by Claims Data Analysis Using Machine Learning," International Journal of Scientific Research in Science, Engineering and Technology, Technoscience Academy, vol. 11(2), pages 119-121, April.
    • Handle: RePEc:ijs:ijsrse:v11:y2024:i2:id:19
    DOI: 10.32628/IJSRSET241129
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