Revolutionizing cardiovascular disease classification through machine learning and statistical methods
The nonparametric Friedman - Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures.
Authors
- Sibarama Panigrahi (ORCID: https://orcid.org/0000-0002-1409-3856)
- Pradeep Kumar Naik (ORCID: https://orcid.org/0000-0001-7044-2427)
- Siddhartha Sathia (ORCID: https://orcid.org/0000-0001-7929-6200)
- Tapan Kumar Behera (ORCID: https://orcid.org/0009-0001-9601-3898)
Institutions
- Sambalpur University (IN)
- National Institute of Technology Rourkela (IN)
- All India Institute of Medical Sciences Bhubaneswar (IN)
Publication Details
- Journal
- Journal of Biopharmaceutical Statistics
- Published
- 2024-11-24
- DOI
- https://doi.org/10.1080/10543406.2024.2429524
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.42