Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X diagnosis: A clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm
Article
Abdar, Moloud, Mehrzadi, Arash, Goudarzi, Milad, Masoudkabir, Farzad, Rundo, Leonardo, Mamouei, Mohammad, Sala, Evis, Khosravi, Abbas, Makarenkov, Vladimir, Acharya, U. Rajendra, Saadatagah, Seyedmohammad, Naderian, Mohammadreza, García, Salvador, Sarrafzadegan, Nizal and Nahavandi, Saeid. 2023. "Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X diagnosis: A clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm." Information Fusion. 97. https://doi.org/10.1016/j.inffus.2023.101813
| Article Title | Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X diagnosis: A clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm |
|---|---|
| ERA Journal ID | 20983 |
| Article Category | Article |
| Authors | Abdar, Moloud, Mehrzadi, Arash, Goudarzi, Milad, Masoudkabir, Farzad, Rundo, Leonardo, Mamouei, Mohammad, Sala, Evis, Khosravi, Abbas, Makarenkov, Vladimir, Acharya, U. Rajendra, Saadatagah, Seyedmohammad, Naderian, Mohammadreza, García, Salvador, Sarrafzadegan, Nizal and Nahavandi, Saeid |
| Journal Title | Information Fusion |
| Journal Citation | 97 |
| Article Number | 101813 |
| Number of Pages | 20 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 1566-2535 |
| 1872-6305 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.inffus.2023.101813 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S1566253523001227 |
| Abstract | Cardiac Syndrome X (CSX) is a very dangerous cardiovascular disease characterized by angina-like chest discomfort and pain on effort despite normal epicardial coronary arteries at angiography. In this study, we used a CSX dataset from the coronary angiography registry of Tehran’s Heart Center at Tehran University of Medical Sciences in Iran to develop several machine learning (ML) methods combined with uncertainty quantification of the obtained results. Uncertainty quantification plays a significant role in both traditional machine learning (ML) and deep learning (DL) studies allowing researchers to create trustable clinical detection systems. We propose a novel Mixture-of-Experts (MoE) model, called Binarized Multi-Gate Mixture of Bayesian Experts (MoBE), which is an effective ensemble technique for accurately classifying CSX data. The proposed binarized multi-gate model relies on a double quantified uncertainty strategy at the feature selection and decision making stages. First, we use a clinician-in-the-loop scenario with a belief-uncertainty paradigm at the feature selection stage. Second, we use Bayesian neural networks (BNNs) as experts in MoBE and Monte Carlo (MC) dropout for gates at the decision making uncertainty quantification stage. The proposed binarized multi-gate model reaches an accuracy of 85% when applied to our benchmark CSX dataset from Tehran’s Heart Center. |
| Keywords | Bayesian neural networks (BNNs); Deep learning; Uncertainty Quantification (UQ); Cardiovascular disease (CVD); Cardiac syndrome X (CSX); Mixture of experts (moE) |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Deakin University |
| Islamic Azad University, Iran | |
| Polytechnic University of Milan, Italy | |
| Tehran University of Medical Sciences, Iran | |
| University of Salerno, Italy | |
| University of Oxford, United Kingdom | |
| University of Cambridge, United Kingdom | |
| University of Quebec, Canada | |
| School of Mathematics, Physics and Computing | |
| Mayo Clinic, United States | |
| University of Granada, Spain | |
| Isfahan University of Medical Sciences, Iran | |
| University of British Columbia, Canada | |
| Harvard University, United States |
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https://research.usq.edu.au/item/z1v48/binarized-multi-gate-mixture-of-bayesian-experts-for-cardiac-syndrome-x-diagnosis-a-clinician-in-the-loop-scenario-with-a-belief-uncertainty-fusion-paradigm
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