Prediction analytics of myocardial infarction through model-driven deep deterministic learning

• Published in: Neural computing & applications 2020-10, Vol.32 (20), p.15909-15928
• Main Authors: Iqbal, Uzair, Wah, Teh Ying, ur Rehman, Muhammad Habib, Shah, Jamal Hussain
• Format: Article
• Language: English
• Subjects: Acceptance; Analytics; Anomalies; Artificial Intelligence; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data integration; Data Mining and Knowledge Discovery; Datasets; Diagnostic software; Diagnostic systems; Electrocardiography; Empirical analysis; Feature extraction; Feature recognition; Flattening; Health care facilities; Heart; Heart attacks; Image Processing and Computer Vision; Learning; Multisensor fusion; Myocardial infarction; Pattern recognition; Predictions; Probability and Statistics in Computer Science; Recent Advances in Deep Learning for Medical Image Processing; Risk analysis
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-019-04400-9

Electrocardiography is the primary diagnostic tool for measuring the malfunction of different heart activities in the form of various cardiac diseases. Some cardiac diseases require special attention due to the urgency and risk factors involved. Myocardial infarction (MI) is one of the cardiac diseases that require robust identification. Early prediction in MI cases without prior history remains to be an ongoing challenge. This article delivers a major novel contribution in the context of predictive classification of flattened T-wave MI cases. Therefore, a novel model-driven deep deterministic learning (MDDDL) approach is proposed. In MDDDL, two different data sets are used for the execution of operational activities in terms of flattened T-wave predictive classification. The first data set is the publicly available Physikalisch-Technische Bundesanstalt (PTB), and the second data set is exclusively obtained from the University of Malaya Medical Centre (UMMC). Firstly, the systematic behaviour of MDDDL is defined in terms of pattern recognition of extracted features between T-wave alternans and flattened T-wave subjects, and then both data sets are merged considering data fusion approach and pre-defined conditions. Afterwards, the empirical approach is adopted in MDDDL evaluation in relation to global acceptance and state-of-the-art comparison. Finally, some qualitative improvements, such as inclusion of a backtracking factor for rapid prediction of flattened anomalies and increasing the number of features along with enhancement of fusion processes to reduce complexity, are required by the MDDDL and should be covered in future works.