CLINICAL DECISION SUPPORT SYSTEM (CDSS) FOR HEART DISEASE DIAGNOSIS AND PREDICTION BY MACHINE LEARNING ALGORITHMS: A SYSTEMATIC LITERATURE REVIEW

Abstract

Clinical decision support systems rooted in machine learning have grabbed much attention due to the use of huge datasets for performing analysis and accurate diagnosis of diseases. Our recent research contributed to the existence of research by having a complete assessment of decision support systems used within the clinical context to diagnose cardiovascular diseases. The researchers separately compiled and analyzed aspects related to clinical decision support systems (CDSS) for cardiac disorders. The primary intention of this systematic review is to point out and critically evaluate the Machine Learning (ML)-based PubMed, Clinical Trial.gov, and Cochrane libraries, an analysis of 400 different studies (1990 to 24 January 2021) is made in which 25 papers met the inclusion criterion. The technique incorporated here NVIVO 10 software gathers and analyzes data. Search filters such as “Machine Learning”, “Clinical decision support systems”, and “heart disease prediction” are used for heart diseases. The results were obtained within clinics in 55% of such investigations, whereas experimental setups were seen in 25% of the studies. The remaining 20% research did not report on the methodologies’ relevance and effectiveness in healthcare situations. The given findings point to CDSS ability to provide accurate interpretations and accurate visual peer representations. The key finding of this analysis report is that although CDSS might be employed in clinical settings it needs to be properly trained with non-ambiguous real-time clinical data. The creation of fuzzy complexes that can conclude on clinical deviations many of which are practical and imposed and tracked in real time is a major focus of future Machine Learning-based CDSS research.