Affiliation:
1. College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
2. School of Computer, BaoJi University of Arts and Sciences, Baoji 721016, China
Abstract
With the rapid development of artificial intelligence, extracting latent information from medical data has become increasingly critical. Cardiovascular disease is now a major threat to human health, being one of the leading causes of mortality. Therefore, developing effective prediction methods for cardiovascular diseases is urgently needed. Current medical approaches primarily focus on disease detection rather than prediction, which limits early intervention. By leveraging computational methods, it is possible to predict cardiovascular disease in advance, enabling timely treatment and potentially reducing the disease’s impact. This study employs machine learning techniques, including Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), to predict cardiovascular diseases as classification problems. These machine learning models are supported by robust mathematical theory, allowing them to handle non-linear classification challenges effectively. The results offer valuable insights for the prevention and early treatment of cardiovascular diseases.
Publisher
Institute of Emerging and Computer Engineers Inc
Reference15 articles.
1. Chen, J.h., Study on early warning Model of Ischemic Cardiovascular and Cerebrovascular Diseases in elderly Health Care population. 2010, The third military Medical University.
2. Zhang, Y.l. and H. Luo, Multiple linear stepwise regression analysis of obesity factors in obese children. Practical preventive medicine, 2008. 15(005): p. 1457-1459.
3. Li, G., Research on Status Evaluation of Oral Health Service and Prediction of Oral Health Human Power. 2004, Sichuan University.
4. Gavhane, A., et al. Prediction of Heart Disease Using Machine Learning. in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). 2018.
5. Patil, M., et al. A Proposed Model for Lifestyle Disease Prediction Using Support Vector Machine. in 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 2018.