TY  - JOUR
AU  - Sun, Weicheng
AU  - Zhang, Ping
AU  - Wang, Zilin
AU  - Li, Dongxu
PY  - 2024
DA  - 2024/05/23
TI  - Machine Learning-Based Prediction of Cardiovascular Diseases
JO  - ICCK Transactions on Internet of Things
T2  - ICCK Transactions on Internet of Things
JF  - ICCK Transactions on Internet of Things
VL  - 2
IS  - 2
SP  - 50
EP  - 54
DO  - 10.62762/TIOT.2024.128976
UR  - https://www.icck.org/article/abs/TIOT.2024.128976
KW  - SVM
KW  - Random Forest
KW  - Machine learning
KW  - Cardiovascular disease prediction
AB  - 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.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Sun2024Machine,
  author = {Weicheng Sun and Ping Zhang and Zilin Wang and Dongxu Li},
  title = {Machine Learning-Based Prediction of Cardiovascular Diseases},
  journal = {ICCK Transactions on Internet of Things},
  year = {2024},
  volume = {2},
  number = {2},
  pages = {50-54},
  doi = {10.62762/TIOT.2024.128976},
  url = {https://www.icck.org/article/abs/TIOT.2024.128976},
  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.},
  keywords = {SVM, Random Forest, Machine learning, Cardiovascular disease prediction},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}