Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)
Article
Alizadehsani, Roohallah, Roshanzamir, Mohamad, Hussain, Sadiq, Khosravi, Abbas, Koohestani, Afsaneh, Zangooei, Mohammad Hossein, Abdar, Moloud, Beykikhoshk, Adham, Shoeibi, Afshin, Zare, Assef, Panahiazar, Maryam, Nahavandi, Saeid, Srinivasan, Dipti, Atiya, Amir F. and Acharya, U. Rajendra. 2021. "Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)." Annals of Operations Research. https://doi.org/10.1007/s10479-021-04006-2
| Article Title | Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020) |
|---|---|
| ERA Journal ID | 41 |
| Article Category | Article |
| Authors | Alizadehsani, Roohallah, Roshanzamir, Mohamad, Hussain, Sadiq, Khosravi, Abbas, Koohestani, Afsaneh, Zangooei, Mohammad Hossein, Abdar, Moloud, Beykikhoshk, Adham, Shoeibi, Afshin, Zare, Assef, Panahiazar, Maryam, Nahavandi, Saeid, Srinivasan, Dipti, Atiya, Amir F. and Acharya, U. Rajendra |
| Journal Title | Annals of Operations Research |
| Number of Pages | 42 |
| Year | 2021 |
| Publisher | Springer |
| Place of Publication | United States |
| ISSN | 0254-5330 |
| 1572-9338 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s10479-021-04006-2 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s10479-021-04006-2 |
| Abstract | Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making. |
| Keywords | Bayesian inference; Uncertainty ; Fuzzy systems; Monte Carlo simulation; Classifcation ·; Machine learning |
| 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 |
| Fasa University, Iran | |
| Dibrugarh University, India | |
| University of Texas at Dallas, United States | |
| K. N. Toosi University of Technology, Iran | |
| Islamic Azad University, Iran | |
| University of California, United States | |
| National University of Singapore | |
| Cairo University, Egypt | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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https://research.usq.edu.au/item/z1v52/handling-of-uncertainty-in-medical-data-using-machine-learning-and-probability-theory-techniques-a-review-of-30-years-1991-2020
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