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

TL;DR

This review paper analyzes 30 years of research on how machine learning and probability theory techniques handle uncertainty in medical data, emphasizing the importance of uncertainty quantification for accurate diagnosis and treatment.

Contribution

It summarizes existing methods for managing uncertainty in medical data and highlights recent advances with deep learning techniques over three decades.

Findings

Limited knowledge on optimal treatment due to uncertainty

Challenges in handling noise in medical data

Increase in deep learning applications for uncertainty

Abstract

Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. 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. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. 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, application of novel deep learning techniques to deal such uncertainties have significantly increased.