Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals

Abstract

Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified. This paper therefore addresses the employment of Monte Carlo dropout within the DL structure to automatically discriminate presymptomatic signs of spinocerebellar ataxia type 2 in saccadic samples obtained from electrooculograms. The current work goes beyond the common incorporation of this special type of dropout into deep neural networks and uses the uncertainty derived from the validation samples to construct a decision tree at the register level of the patients. The decision tree built from the uncertainty estimates obtained a classification accuracy of 81.18% in automatically discriminating control, presymptomatic and sick classes. This paper proposes a novel method to address both uncertainty quantification and explainability to develop reliable healthcare support systems.

An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions

“…Wang and Rocková [11] 2020 N/A N/A Semi-parametric BvM (Bernstein-von Mises theorem) Chen et al [12] 2020 Text analysis Classification N/A Stoean et al [13] 2020 Medical Classification MC dropout Hirschfeld et al [14] 2020 Molecular property Prediction N/A Huo et al [15] 2020 Mobile activity Recognition MEL (maximum entropy learning) Schwaiger et al [16] 2020 Vision and image processing Out-of-distribution (OOD) EDL (Evidential Deep Learning) LC (Learned Confidence) Edupuganti et al [17] 2020 Medical Segmentation Monte-Carlo sampling Aseeri [18] 2021 Medical Classification MC dropout Shamsi et al [19] 2021 Medical Classification Bayesian Ensemble Hoffmann et al [20] 2021 Computational optical Segmentation Ensemble learning Abdar et al [21] 2021 Medical Classification TWDBDL (Three-Way Decision-based Bayesian DL) Phan et al [22] 2021 Sleep staging Classification Entropy-based confidence quantification Fig. (2) The uncertainty confusion matrix [35].…”

Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks

“…Another CNN with MCD for probabilistic wind power forecasting is presented in [18]. After training a convolutional long short-term memory network architecture with MCD, the uncertainty estimates of single medical records were used to predict the class for the higher level of a patient register [19,20]. As far as we are aware, MCD has not been used as an uncertainty measure in the context of ESNs applied to time series forecasting.…”

An Improved Hybrid Approach for Handling Class Imbalance Problem

“…Machine learning is a well-known research domain in computers science to use a variety of algorithms to extract useful information among huge raw data. These algorithms have widely been applied to different subjects such as medical data analysis [1][2][3][4], class noise detection [5], image processing [6], sentiment analysis [7,8], signal processing [9,10], road accident analysis [11], social data mining [12] and many more. However, in most cases, we may not have a balanced dataset.…”