Title: Artificial intelligence enabled healthcare data analysis for chronic heart disease detection: an evaluation

Authors: Ahmed Tazi; Soly Mathew Biju; Farhad Oroumchian; Manoj Kumar

Addresses: Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, UAE ' Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, UAE ' Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, UAE ' Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, UAE

Abstract: Chronic heart diseases are a leading cause of death worldwide. This paper examines recently published articles on the use of AI and machine-learning to detect chronic heart diseases. The main findings of these papers are summarised to assist researchers in developing new technology using AI and machine learning. The summary includes information on the technologies used in each research paper, the year of publication, the type of heart disease, the machine learning techniques employed and the advantages and limitations of each approach. The research followed the Preferred Reporting Items for Systematic Review and Meta Analysis (PRISMA) standard and evaluated diagnostic studies using QUADAS-2, the quality assessment of diagnostic accuracy studies. We used the intelligent web-based tool 'Rayyan' for data extraction and processing. The results demonstrate that machine learning algorithms have an Area Under the Curve (AUC) between 0.80 and 0.90, which is acceptable for chronic heart conditions overall.

Keywords: AI; healthcare; machine learning; systematic review and meta analysis; diagnostic accuracy; area under the curve; chronic heart condition.

DOI: 10.1504/IJGUC.2024.137912

International Journal of Grid and Utility Computing, 2024 Vol.15 No.2, pp.198 - 210

Received: 28 Feb 2023
Accepted: 09 Jul 2023
Published online: 08 Apr 2024 *

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