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

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.

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

“…Nowadays, deep 2D networks are used for various medical applications such as diagnosis of COVID-19 in CT and X-ray [ 46 , 47 ], and autism spectrum disorders from MRI modalities [ 48 ]. First, in 2012, Krizovsky et al [ 49 ] suggested this network to solve image classification problems, and then quickly used similar networks for different tasks such as medical image classification, in an effort to obviate the difficulties of previous networks and solve more intricate problems with better performance.…”

Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images

“…Predictive uncertainty estimation (or simply referred as uncertainty estimation in this work) for machine learning models has been widely studied in the literature [16].In general, uncertainty sources can be categorized in aleatoric and epistemic. Aleatoric uncertainty refers to the uncertainty inherent in the measurements [17].…”

Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems

“…Data encryption is usually done by chaotic or hyper-chaotic methods [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. In some researches, chaotic or hyper-chaotic systems are used to encrypt peripheral data [ 38 , 39 , 40 , 41 , 42 , 43 ]. In recent years, researchers have focused on the encryption of medical data using chaotic methods [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ].…”

“…In recent years, researchers have focused on the encryption of medical data using chaotic methods [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Medical data contains important information about patients [ 41 , 44 , 45 , 46 ]. Therefore, the confidentiality of medical information is essential when sending it through telecommunication channels.…”

“…Data encryption is usually done by chaotic or hyper-chaotic methods . In some researches, chaotic or hyper-chaotic systems are used to encrypt peripheral data [38][39][40][41][42][43]. In recent years, researchers have focused on the encryption of medical data using chaotic methods [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35].…”