Faizal, Aizatul Shafiqah Mohd and Thevarajah, T. Malathi and Khor, Sook Mei and Chang, Siow-Wee (2021) A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Computer Methods and Programs in Biomedicine, 207. ISSN 0169-2607, DOI I10.1016/j.cmpb.2021.106190.
Full text not available from this repository.Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Tra-ditionally, statistical models are used commonly in the risk prediction and assessment of CVD. However, the adoption of artificial intelligent (AI) approach is rapidly taking hold in the current era of technology to evaluate patient risks and predict the outcome of CVD. In this review, we outline various conventional risk scores and prediction models and do a comparison with the AI approach. The strengths and limita-tions of both conventional and AI approaches are discussed. Besides that, biomarker discovery related to CVD are also elucidated as the biomarkers can be used in the risk stratification as well as early detection of the disease. Moreover, problems and challenges involved in current CVD studies are explored. Lastly, future prospects of CVD risk prediction and assessment in the multi-modality of big data integrative ap-proaches are proposed. (c) 2021 Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Funders: | University of Malaya Impact Oriented Interdisciplinary Research Grant [IIRG020B2019] |
| Uncontrolled Keywords: | Cardiovascular diseases; Risk prediction; Artificial intelligence; Machine learning; Deep learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Medicine > Pathology Department Faculty of Science > Institute of Biological Sciences Faculty of Science > Department of Chemistry |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Apr 2022 00:22 |
| Last Modified: | 18 Apr 2022 00:22 |
| URI: | http://eprints.um.edu.my/id/eprint/26757 |
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