  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.
