In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing. DL models have also been intensely used in different tasks of healthcare such as disease diagnostics and treatments. Deep learning techniques have surpassed other machine learning algorithms and proved to be the ultimate tools for many state-of-the-art applications. Despite all that success, classical deep learning has limitations and their models tend to be very confident about their predicted decisions because it does not know when it makes mistake. For the healthcare field, this limitation can have a negative impact on models predictions since almost all decisions regarding patients and diseases are sensitive. Therefore, Bayesian deep learning (BDL) has been developed to overcome these limitations. Unlike classical DL, BDL uses probability distributions for the model parameters, which makes it possible to estimate the whole uncertainties associated with the predicted outputs. In this regard, BDL offers a rigorous framework to quantify all sources of uncertainties in the model. This study reviews popular techniques of using Bayesian deep learning with their benefits and limitations. It also reviewed recent deep learning architecture such as Convolutional Neural Networks and Recurrent Neural Networks. In particular, the applications of Bayesian deep learning in healthcare have been discussed such as its use in medical imaging tasks, clinical signal processing, medical natural language processing, and electronic health records. Furthermore, this paper has covered the deployment of Bayesian deep learning for some of the widespread diseases. This paper has also discussed the fundamental research challenges and highlighted some research gaps in both the Bayesian deep learning and healthcare perspective.
Imbalanced data classification is a common issue in data mining where the classifiers are skewed towards the larger data class. Classification of high-dimensional skewed (imbalanced) data is of great interest to decisionmakers as it is more difficult to. The dimension reduction method, a process in which variables are reduced, allows high dimensional datasets to be interpreted more easily with a certain loss. This study, a method combining SMOTE oversampling with principal component analysis is proposed to solve the imbalance problem in high dimensional data. Three classification algorithms consisting of Logistic Regression, K-Nearest Neighbor, Decision Tree methods and two separate datasets were utilized to evaluate the suggested method's efficacy and determine the classifiers' performance. Respectively, raw datasets, converted datasets by PCA, SMOTE and SMOTE+PCA (SMOTE and PCA) methods, were analyzed with the given algorithms. Analyzes were made using WEKA. Analysis results suggest that almost all classification algorithms improve their classification performance using PCA, SOMTE, and SMOTE+PCA methods. However, the SMOTE method gave more efficient results than PCA and PCA+SMOTE methods for data rebalancing. Experimental results also suggest that K-Nearest Neighbor classifier provided higher classification performance compared to other algorithms.
Deep sequential (DS) models are extensively employed for forecasting time series data since the dawn of the deep learning era, and they provide forecasts for the values required in subsequent time steps. DS models, unlike other traditional statistical models for forecasting time series data, can learn hidden patterns in temporal sequences and have the memorizing data from prior time points. Given the widespread usage of deep sequential models in several domains, a comprehensive study describing their applications is necessary. This work presents a comprehensive review of contemporary deep learning time series models, their performance in diverse domains, and an investigation of the models that were employed in various applications. Three deep sequential models, namely, artificial neural network (ANN), long short-term memory (LSTM), and temporal-conventional neural network (TCNN) along with their applications for forecasting time series data, are elaborated. We showed a comprehensive comparison between such models in terms of application fields, model structure and activation functions, optimizers, and implementation, with a goal of learning more about the optimal model used. Furthermore, the challenges and perspectives of future development of deep sequential models are presented and discussed. We conclude that the LSTM model is widely employed, particularly in the form of a hybrid model, in which the most accurate predictions are made when the shape of hybrids is used as the model.
Convolutional neural networks (CNNs) have become a popular choice for various image classification applications. However, the multi-layer perceptron mixer (MLP-Mixer) architecture has been proposed as a promising alternative, particularly for large datasets. Despite its advantages in handling large datasets and models, MLP-Mixer models have limitations when dealing with small datasets. This study aimed to quantify and evaluate the uncertainty associated with MLP-Mixer models for small datasets using Bayesian deep learning (BDL) methods to quantify uncertainty and compare the results to existing CNN models. In particular, we examined the use of variational inference and Monte Carlo dropout methods. The results indicated that BDL can improve the performance of MLP-Mixer models by 9.2 to 17.4% in term of accuracy across different mixer models. On the other hand, the results suggest that CNN models tend to have limited improvement or even decreased performance in some cases when using BDL. These findings suggest that BDL is a promising approach to improve the performance of MLP-Mixer models, especially for small datasets.
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