Cardiovascular Disease Long-Term Care Risk Prediction by Claims Data Analysis Using Machine Learning
DOI:
https://doi.org/10.32628/IJSRSET241129Keywords:
Cardiovascular Disease Prediction, Ai, Machine Learning, Internet of Things, HRFLM, HealthcareAbstract
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.
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Youness Khourdifi and Mohamed Bahajm," Coronary illness Expectation and Arrangement Utilizing AI Calculations Improved by Molecule Multitude Streamlining and Insect State Enhancement", Global Diary of Smart Designing and Frameworks, Vol.12, No.1, 2019 DOI: 10.22266/ijies2019.0228.24. DOI: https://doi.org/10.22266/ijies2019.0228.24
Shadman Nashif, Md. Rakib Raihan, Md. Rasedul Islam and Mohammad Hasan Imam," Coronary illness Location by Utilizing AI Calculations and a Constant Cardiovascular Wellbeing Observing Framework", DOI: 10.4236/wjet.2018.64057 Nov. 22, 2018 World Diary of Designing and Innovation.
Li Yang, Haibin Wu, Xiaoqing Jin, and Pinpin Zheng4 et al., "Investigation of cardiovascular illness forecast model in view of arbitrary backwoods in eastern China ", Logical Reports (2020) 10:5245 DOI: https://doi.org/10.1038/s41598-020-62133-5
S. Mohan, C. Thirumalai, and G. Srivastava, "Powerful Coronary illness Forecast Utilizing Cross breed AI Strategies," in IEEE Access, vol. 7, pp. 81542-81554, 2019, doi: 10.1109/ACCESS.2019.2923707. DOI: https://doi.org/10.1109/ACCESS.2019.2923707
Amin Ul Haq and Jian Ping Li, Muhammad Hammad Memon, Shah Nazir , and Ruinan Sun , "A Half and half Keen Framework Structure for the Expectation of Coronary illness Utilizing AI Calculations", Hindawi Portable Data Frameworks Volume 2018
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Copyright (c) 2024 Sourabh Pawar, Tejas Pawar, Pranav More, Prof. Priti Rathod (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
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