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; U Rajendra Acharya

doi:10.1007/s10479-021-04006-2

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

Ann Oper Res. 2021 Mar 21:1-42. doi: 10.1007/s10479-021-04006-2. Online ahead of print.

Authors

Roohallah Alizadehsani¹, Mohamad Roshanzamir², Sadiq Hussain³, Abbas Khosravi¹, Afsaneh Koohestani¹, Mohammad Hossein Zangooei⁴, Moloud Abdar¹, Adham Beykikhoshk⁵, Afshin Shoeibi^{6

7}, Assef Zare⁸, Maryam Panahiazar⁹, Saeid Nahavandi¹, Dipti Srinivasan¹⁰, Amir F Atiya¹¹, U Rajendra Acharya^{12

13

14}

Affiliations

¹ Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia.
² Department of Computer Engineering, Faculty of Engineering, Fasa University, 74617-81189 Fasa, Iran.
³ System Administrator, Dibrugarh University, Dibrugarh, Assam 786004 India.
⁴ University of Texas At Dallas, Dallas, USA.
⁵ Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia.
⁶ Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran.
⁷ Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran.
⁸ Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran.
⁹ Institute for Computational Health Sciences, University of California, San Francisco, USA.
¹⁰ Dept. of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576 Singapore.
¹¹ Department of Computer Engineering, Faculty of Engineering, Cairo University, Cairo, 12613 Egypt.
¹² Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.
¹³ Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore.
¹⁴ Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.

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; Classification; Fuzzy systems; Machine learning; Monte Carlo simulation; Uncertainty.