Analysis of Cardiovascular Diseases Prediction Using Machine Learning Classification Algorithms
- Title
- Analysis of Cardiovascular Diseases Prediction Using Machine Learning Classification Algorithms
- Creator
- Srivastava S.; Upreti K.; Shanbhog M.
- Description
- Worldwide healthcare systems have faced enormous hurdles because of the COVID-19 pandemic, especially when it comes to treating individuals who already have pre-existing disorders such as cardiovascular diseases (CVDs). Prioritizing medical therapies and resources for COVID-19 patients who are at increased risk of mortality from underlying CVDs requires early identification. In this work, we investigate how well three machine learning algorithms-, Random Forest, XGBoost, and Logistic Regression-predict death in COVID-19 patients who already have cardiovascular disease. We performed grid search and cross-validation using a dataset of clinical and demographic features of COVID-19 patients with and without CVDs to reduce overfitting and maximize model performance. Our findings show that among patients with CVDs, Logistic Regression had the best accuracy in predicting COVID-19 fatality, followed by Random Forest and Decision Tree coming in a close second. These results highlight how machine learning algorithms can help clinical professionals detect high-risk COVID-19 patients who have underlying cardiovascular diseases (CVDs), enable prompt interventions, and enhance patient outcomes. 2024 IEEE.
- Source
- Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Cardiovascular Diseases; COVID-19; Cross-validation; Grid Search; Healthcare Decision Making; Logistic Regression; Machine Learning; Random Forest; XGBoost Fatality Prediction
- Coverage
- Srivastava S., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Shanbhog M., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038943-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Srivastava S.; Upreti K.; Shanbhog M., “Analysis of Cardiovascular Diseases Prediction Using Machine Learning Classification Algorithms,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/19354.