Development of nonlaboratory-based risk prediction models for cardiovascular diseases using conventional and machine learning approaches

• Main Authors: Mirza Rizwan, Sajid, Almehmadi, Bader A., Sami, Waqas, Alzahrani, Mansour K., Noryanti, Muhammad, Chesneau, Christophe, Hanif, Anif, Khan, Arshad Ali, Shahbaz, Ahmad
• Format: Article
• Language: English
• Published: MDPI AG 2021
• Subjects: Q Science (General); QA Mathematics; RA1001 Forensic Medicine. Medical jurisprudence. Legal medicine
• Online Access: http://umpir.ump.edu.my/id/eprint/33125/1/Development%20of%20nonlaboratory-based%20risk%20prediction%20models%20for%20cardiovascular.pdf; http://umpir.ump.edu.my/id/eprint/33125/; https://doi.org/10.3390/ijerph182312586; https://doi.org/10.3390/ijerph182312586

Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low–middle-income cou...