A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. (August 2021)

• Record Type: Journal Article
• Authors: Mohd Faizal, Aizatul Shafiqah; Thevarajah, T. Malathi; Khor, Sook Mei; Chang, Siow-Wee
• Is Part Of: Computer methods and programs in biomedicine. Volume 207(2021)
• Publication Date: 2021-08
• Subjects: Cardiovascular diseases -- Risk prediction -- Artificial intelligence -- Machine learning -- Deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106190
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A review of conventional and artificial intelligent (AI) risk prediction models in cardiovascular disease (CVD). Conventional risk prediction models are still the current gold standard and commonly used today. AI risk prediction approaches such as machine learning and deep learning are gaining more attention nowadays due to their ability to develop a standardized predictive model that could augment the decision making and improve in patient care management. CVD Biomarkers are important in the risk stratification for early detection and automated rapid analysis of the disease. Future prospect of the CVD risk prediction model - integration of multi-modality data using AI approach. Abstract: Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Traditionally, 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 limitations 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 approaches are proposed.