Korean Journal of Artificial Intelligence (한국인공지능학회지)

Korea Artificial Intelligence Association (한국인공지능학회)
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Development of Big Data-based Cardiovascular Disease Prediction Analysis Algorithm

  • Kyung-A KIM (Department of Medical Artificial Intelligence, Eulji University) ;
  • Dong-Hun HAN (Department of Medical Artificial Intelligence, Eulji University) ;
  • Myung-Ae CHUNG (Department of BigData Medical Convergence, Eulji University)
  • Received : 2023.07.31
  • Accepted : 2023.09.05
  • Published : 2023.09.30
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Abstract

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

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Acknowledgement

This paper was supported by IITP (Institute of information & Communications Technology Planning & Evaluation(www.iitp.kr), Korea) [Project Number: 2022-0-00317]. This paper was supported by the research grant of the KODISA scholarship foundation in 2023.

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