A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data. (July 2022)

• Record Type: Journal Article
• Authors: Azmi, Javed; Arif, Muhammad; Nafis, Md Tabrez; Alam, M. Afshar; Tanweer, Safdar; Wang, Guojun
• Is Part Of: Medical engineering & physics. Volume 105(2022)
• Publication Date: 2022-07
• Subjects: Cardiovascular disease -- CVD -- Classification -- Machine learning -- Disease prediction; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2022.103825
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Review on Machine Learning approaches for Cardiovascular Disease. Enormous Data being generated from various healthcare related devices. Study of recent development in Healthcare Informatics. Review of latest literature on diseases pertaining to Cardio. Abstract: There is a considerable rise in cardiovascular diseases in the world. It is pertinently essential to make cardiovascular prediction accurate to the maximum. A forecast based on machine learning techniques can be beneficial in detecting cardiovascular disease (CVD) with maximum precision and accuracy. The disease's effective prediction helps in early diagnosis, which cuts down the mortality rate. A health history and the causes of heart disease require the efficient detection and prediction of CVD. Data analytics is beneficial for making predictions based on a massive amount of data, and it aids health clinics in disease prognosis. Regularly, a large volume of patient-related data is maintained. The information gathered can be used to forecast the emergence of upcoming diseases. Our study presents a detailed comparative study of Cardiovascular Disease by comparing the various machine learning techniques mainly comprising of classification and predictive algorithms. The study shows an in-depth analysis of around forty-one papers related to cardiovascular disease by using machine learning techniques. This study evaluates the selected publications rigorously and identifies gaps in the available literature, making it useful for researchers to develop and apply in clinical fields, primarily on datasets related to heart disease. The current study will aid medical practitioners in predicting heart threats ahead of time, allowing them to take preventative measures.