Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Roohallah Alizadehsani (), Mohamad Roshanzamir, Sadiq Hussain, Abbas Khosravi, Afsaneh Koohestani, Mohammad Hossein Zangooei, Moloud Abdar, Adham Beykikhoshk, Afshin Shoeibi, Assef Zare, Maryam Panahiazar, Saeid Nahavandi, Dipti Srinivasan, Amir F. Atiya and U. Rajendra Acharya
Additional contact information
Roohallah Alizadehsani: Deakin University
Mohamad Roshanzamir: Fasa University
Sadiq Hussain: Dibrugarh University
Abbas Khosravi: Deakin University
Afsaneh Koohestani: Deakin University
Mohammad Hossein Zangooei: University of Texas At Dallas
Moloud Abdar: Deakin University
Adham Beykikhoshk: Deakin University
Afshin Shoeibi: Ferdowsi University of Mashhad
Assef Zare: Islamic Azad University
Maryam Panahiazar: University of California
Saeid Nahavandi: Deakin University
Dipti Srinivasan: National University of Singapore
Amir F. Atiya: Cairo University
U. Rajendra Acharya: Ngee Ann Polytechnic

Annals of Operations Research, 2024, vol. 339, issue 3, No 2, 1077-1118

Abstract: 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: Uncertainty; Bayesian inference; Fuzzy systems; Monte Carlo simulation; Classification; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-021-04006-2

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