A hybrid stacked autoencoder and support vector machines-based expert system for heart failure detection.

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Bibliographic Details
Title: A hybrid stacked autoencoder and support vector machines-based expert system for heart failure detection.
Authors: Kamal MM; School of Electronic and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China. mianmuhammadkamal@qzuie.edu.cn., Khan W; Department of Electrical Engineering, University of Science and Technology Bannu, Bannu, Pakistan., Shambour QY; Department of Data Science and Artificial Intelligence, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan., Gafar MA; Faculty of Medicine (FOM), Cairo University, Giza, Egypt., Alarifi A; Computer Science and Engineering Department, College of Applied Studies, Riyadh, 11437, Saudi Arabia., Sheraz M; Centre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Cyberjaya, 63100, Selangor, Malaysia., Chuah TC; Centre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Cyberjaya, 63100, Selangor, Malaysia. tcchuah@mmu.edu.my.
Source: Scientific reports [Sci Rep] 2026 Jan 08; Vol. 16 (1), pp. 3886. Date of Electronic Publication: 2026 Jan 08.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-025-34430-4