Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients' Recovery

Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction

Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM's network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model's efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM's superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.

Exploring the potential of learning methods and recurrent dynamic model with vaccination: A comparative case study of COVID-19 in Austria, Brazil, and China

In order to effectively manage infectious diseases, it is crucial to understand the interplay between disease dynamics and human conduct. Various factors can impact the control of an epidemic, including social interventions, adherence to health protocols, mask-wearing, and vaccination. This article presents the development of an innovative hybrid model, known as the Combined Dynamic-Learning Model, that integrates classical recurrent dynamic models with four different learning methods. The model is composed of two approaches: The first approach introduces a traditional dynamic model that focuses on analyzing the impact of vaccination on the occurrence of an epidemic, and the second approach employs various learning methods to forecast the potential outcomes of an epidemic. Furthermore, our numerical results offer an interesting comparison between the traditional approach and modern learning techniques. Our classic dynamic model is a compartmental model that aims to analyze and forecast the diffusion of epidemics. The model we propose has a recurrent structure with piecewise constant parameters and includes compartments for susceptible, exposed, vaccinated, infected, and recovered individuals. This model can accurately mirror the dynamics of infectious diseases, which enables us to evaluate the impact of restrictive measures on the spread of diseases. We conduct a comprehensive dynamic analysis of our model. Additionally, we suggest an optimal numerical design to determine the parameters of the system. Also, we use regression tree learning, bidirectional long short-term memory, gated recurrent unit, and a combined deep learning method for training and evaluation of an epidemic. In the final section of our paper, we apply these methods to recently published data on COVID-19 in Austria, Brazil, and China from 26 February 2021 to 4 August 2021, which is when vaccination efforts began. To evaluate the numerical results, we utilized various metrics such as RMSE and R-squared. Our findings suggest that the dynamic model is ideal for long-term analysis, data fitting, and identifying parameters that impact epidemics. However, it is not as effective as the supervised learning method for making long-term forecasts. On the other hand, supervised learning techniques, compared to dynamic models, are more effective for predicting the spread of diseases, but not for analyzing the behavior of epidemics.

Dual-stream EfficientNet with adversarial sample augmentation for COVID-19 computer aided diagnosis

Though a series of computer aided measures have been taken for the rapid and definite diagnosis of 2019 coronavirus disease (COVID-19), they generally fail to achieve high enough accuracy, including the recently popular deep learning-based methods. The main reasons are that: (a) they generally focus on improving the model structures while ignoring important information contained in the medical image itself; (b) the existing small-scale datasets have difficulty in meeting the training requirements of deep learning. In this paper, a dual-stream network based on the EfficientNet is proposed for the COVID-19 diagnosis based on CT scans. The dual-stream network takes into account the important information in both spatial and frequency domains of CT scans. Besides, Adversarial Propagation (AdvProp) technology is used to address the insufficient training data usually faced by the deep learning-based computer aided diagnosis and also the overfitting issue. Feature Pyramid Network (FPN) is utilized to fuse the dual-stream features. Experimental results on the public dataset COVIDx CT-2A demonstrate that the proposed method outperforms the existing 12 deep learning-based methods for COVID-19 diagnosis, achieving an accuracy of 0.9870 for multi-class classification, and 0.9958 for binary classification. The source code is available at https://github.com/imagecbj/covid-efficientnet.

Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty

Background

Frailty is a dynamic and complex geriatric condition characterized by multi-domain declines in physiological, gait and cognitive function. This study examined whether digital health technology can facilitate frailty identification and improve the efficiency of diagnosis by optimizing analytical and machine learning approaches using select factors from comprehensive geriatric assessment and gait characteristics.

Methods

As part of an ongoing study on observational study of Aging, we prospectively recruited 214 individuals living independently in the community of Southern China. Clinical information and fragility were assessed using comprehensive geriatric assessment (CGA). Digital tool box consisted of wearable sensor-enabled 6-min walk test (6MWT) and five machine learning algorithms allowing feature selections and frailty classifications.

Results

It was found that a model combining CGA and gait parameters was successful in predicting frailty. The combination of these features in a machine learning model performed better than using either CGA or gait parameters alone, with an area under the curve of 0.93. The performance of the machine learning models improved by 4.3-11.4% after further feature selection using a smaller subset of 16 variables. SHapley Additive exPlanation (SHAP) dependence plot analysis revealed that the most important features for predicting frailty were large-step walking speed, average step size, age, total step walking distance, and Mini Mental State Examination score.

Conclusion

This study provides evidence that digital health technology can be used for predicting frailty and identifying the key gait parameters in targeted health assessments.

Analysis of COVID-19 Death Cases Using Machine Learning

COVID-19 has threatened the existence of human life for more than the last 2 years. More than 460 million confirmed cases and 6 million deaths have been reported worldwide due to COVID-19. To measure the severity of the COVID-19, the mortality rate plays an important role. Understanding the nature of COVID-19 and forecasting the death cases of COVID-19 require more investigation of the real effect for different risk factors. In this work, various regression machine learning models are proposed to extract the relationship between different factors and the death rate of COVID-19. The optimal regression tree algorithm employed in this work estimates the impact of essential causal variables that significantly affect the mortality rates. We have generated a real-time forecast for the death case of COVID-19 using machine learning techniques. The analysis is evaluated with the well-known regression models XGBoost, Random Forest, and SVM on the data sets of the US, India, Italy, and three continents Asia, Europe, and North America. The results show that the models can be used to forecast the death cases for the near future in case of an epidemic like Novel Coronavirus.

Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran

Background and Aims

Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID-19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID-19 by comparing the accuracy of machine learning (ML) models.

Methods

It is a cross-sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment.

Results

Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F-score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death.

Conclusion

The development of software systems based on NB will be effective to predict the survival of COVID-19 patients.

Efficient prediction of early-stage diabetes using XGBoost classifier with random forest feature selection technique

Diabetes is one of the most common and serious diseases affecting human health. Early diagnosis and treatment are vital to prevent or delay complications related to diabetes. An automated diabetes detection system assists physicians in the early diagnosis of the disease and reduces complications by providing fast and precise results. This study aims to introduce a technique based on a combination of multiple linear regression (MLR), random forest (RF), and XGBoost (XG) to diagnose diabetes from questionnaire data. MLR-RF algorithm is used for feature selection, and XG is used for classification in the proposed system. The dataset is the diabetic hospital data in Sylhet, Bangladesh. It contains 520 instances, including 320 diabetics and 200 control instances. The performance of the classifiers is measured concerning accuracy (ACC), precision (PPV), recall (SEN, sensitivity), F1 score (F1), and the area under the receiver-operating-characteristic curve (AUC). The results show that the proposed system achieves an accuracy of 99.2%, an AUC of 99.3%, and a prediction time of 0.04825 seconds. The feature selection method improves the prediction time, although it does not affect the accuracy of the four compared classifiers. The results of this study are quite reasonable and successful when compared with other studies. The proposed method can be used as an auxiliary tool in diagnosing diabetes.

Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm

The chest X-ray images provide vital information about the congestion cost-effectively. We propose a novel Hybrid Deep Learning Algorithm (HDLA) framework for automatic lung disease classification from chest X-ray images. The model consists of steps including pre-processing of chest X-ray images, automatic feature extraction, and detection. In a pre-processing step, our goal is to improve the quality of raw chest X-ray images using the combination of optimal filtering without data loss. The robust Convolutional Neural Network (CNN) is proposed using the pre-trained model for automatic lung feature extraction. We employed the 2D CNN model for the optimum feature extraction in minimum time and space requirements. The proposed 2D CNN model ensures robust feature learning with highly efficient 1D feature estimation from the input pre-processed image. As the extracted 1D features have suffered from significant scale variations, we optimized them using min-max scaling. We classify the CNN features using the different machine learning classifiers such as AdaBoost, Support Vector Machine (SVM), Random Forest (RM), Backpropagation Neural Network (BNN), and Deep Neural Network (DNN). The experimental results claim that the proposed model improves the overall accuracy by 3.1% and reduces the computational complexity by 16.91% compared to state-of-the-art methods.

Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning

Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques are more effective than the reverse-transcription polymerase chain reaction (RT-PCR) method in detecting coronavirus. Due to the limited availability of medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 patients from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2, where CNN is used to extract complex features from samples and classify them using RNN. In our experiments, the VGG19-RNN architecture outperformed all other networks in terms of accuracy. Finally, decision-making regions of images were visualized using gradient-weighted class activation mapping (Grad-CAM). The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff. 

All the data used during the study are openly available from the Mendeley data repository at https://data.mendeley.com/datasets/mxc6vb7svm. For further research, we have made the source code publicly available at https://github.com/Asraf047/COVID19-CNN-RNN.

Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans

Coronavirus, a virus that spread worldwide rapidly and was eventually declared a pandemic. The rapid spread made it essential to detect Coronavirus infected people to control the further spread. Recent studies show that radiological images such as X-Rays and CT scans provide essential information in detecting infection using deep learning models. This paper proposes a shallow architecture based on Capsule Networks with convolutional layers to detect COVID-19 infected persons. The proposed method combines the ability of the capsule network to understand spatial information with convolutional layers for efficient feature extraction. Due to the model's shallow architecture, it has 23M parameters to train and requires fewer training samples. The proposed system is fast and robust and correctly classifies the X-Ray images into three classes, i.e. COVID-19, No Findings, and Viral Pneumonia. Experimental results on the X-Ray dataset show that our model performs well despite having fewer samples for the training and achieved an average accuracy of 96.47% for multi-class and 97.69% for binary classification on 5-fold cross-validation. The proposed model would be useful to researchers and medical professionals for assistance and prognosis for COVID-19 infected patients.

Neurodegenerative diseases detection and grading using gait dynamics

Detection of neurodegenerative diseases such as Parkinson's disease, Huntington's disease, Amyotrophic Lateral Sclerosis, and grading of these diseases' severity have high clinical significance. These tasks based on walking analysis stand out compared to other methods due to their simplicity and non-invasiveness. This study has emerged to realize an artificial intelligence-based disease detection and severity prediction system for neurodegenerative diseases using gait features obtained from gait signals. For the detection of the disease, the problem is divided into parts which are subgroups of 4 classes consisting of Parkinson's, Huntington's, Amyotrophic Lateral Sclerosis diseases, and the control group. In addition, the disease vs. control subgroup where all diseases are collected under a single label, the subgroups where each disease is separately against the control group. For disease severity grading, each disease was divided into subgroups and a solution was sought for the prediction problem mentioned by various machine and deep learning methods separately for each group. In this context, the resulting detection performance was measured by the metrics of Accuracy, F1 Score, Precision, and Recall while the resulting prediction performance was measured by the metrics such as R, R2, MAE, MedAE, MSE, and RMSE.

Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19

COVID-19 has impacted billions of people and health care systems globally. However, there is currently no publicly available chatbot for patients and care providers to determine the potential severity of a COVID-19 infection or the possible biological system responses and comorbidities that can contribute to the development of severe cases of COVID-19. This preliminary investigation assesses this lack of a COVID-19 case-by-case chatbot into consideration when building a decision tree with binary classification that was stratified by age and body system, viral infection, comorbidities, and any manifestations. After reviewing the relevant literature, a decision tree was constructed using a suite of tools to build a stratified framework for a chatbot application and interaction with users. A total of 212 nodes were established that were stratified from lung to heart conditions along body systems, medical conditions, comorbidities, and relevant manifestations described in the literature. This resulted in a possible 63,360 scenarios, offering a method toward understanding the data needed to validate the decision tree and highlighting the complicated nature of severe cases of COVID-19. The decision tree confirms that stratification of the viral infection with the body system while incorporating comorbidities and manifestations strengthens the framework. Despite limitations of a viable clinical decision tree for COVID-19 cases, this prototype application provides insight into the type of data required for effective decision support.

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R ² score values.

Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

Coronavirus disease 2019 (COVID-19) has produced a global pandemic, which has devastating effects on health, economy and social interactions. Despite the less contraction and spread of COVID-19 in Africa compared to some other continents in the world, Africa remains amongst the most vulnerable regions due to less technology and unequipped or poor health system. Recent happenings showed that COVID-19 may stay for years owing to the discoveries of new variants (such as Omicron) and new wave of infections in several countries. Therefore, accurate prediction of new cases is vital to make informed decisions and in evaluating the measures that should be implemented. Studies on COVID-19 prediction are limited in Africa despite the risks and dangers that the virus possessed. Hence, this study was performed to predict daily COVID-19 cases in 10 African countries spread across the north, south, east, west and central Africa considering countries with few and large number of daily COVID-19 cases. Machine learning (ML) models due to their nonlinearity and accurate prediction capabilities were employed for this purpose, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and conventional multiple linear regression (MLR) models. As any other natural process, the COVID-19 pandemic may contain both linear and nonlinear aspects. In such circumstances, neither nonlinear (ML) nor linear (MLR) models could be sufficient; hence, combining both ML and MLR models may produce better accuracy. Consequently, to improve the prediction efficiency of the ML models, novel ensemble approaches including ANN-E and SVM-E were employed. The advantage of using ensemble approaches is that they provide collective benefits of all the standalone models, thereby reducing their weaknesses and enhancing their prediction capabilities. The obtained results showed that ANFIS led to better prediction performance with MAD = 0.0106, MSE = 0.0003, RMSE = 0.0185 and R2 = 0.9059 in the validation step. The results of the proposed ensemble approaches demonstrated very high improvements in predicting the COVID-19 pandemic in Africa with MAD = 0.0073, MSE = 0.0002, RMSE = 0.0155 and R2 = 0.9616. The ANN-E improved the standalone models performance in the validation step up to 10%, 14%, 42%, 6%, 83%, 11%, 7%, 5%, 7% and 31% for Morocco, Sudan, Namibia, South Africa, Uganda, Rwanda, Nigeria, Senegal, Gabon and Cameroon, respectively. This study results offer a solid foundation in the application of ensemble approaches for predicting COVID-19 pandemic across all regions and countries in the world.

Machine learning-based IoT system for COVID-19 epidemics

The planet earth has been facing COVID-19 epidemic as a challenge in recent time. It is predictable that the world will be fighting the pandemic by taking precautions steps before an operative vaccine is found. The IoT produces huge data volumes, whether private or public, through the invention of IoT devices in the form of smart devices with an improved rate of IoT data generation. A lot of devices interact with each other in the IoT ecosystem through the cloud or servers. Various techniques have been presented in recent time, using data mining approach have proven help detect possible cases of coronaviruses. Therefore, this study uses machine learning technique (ABC and SVM) to predict COVID-19 for IoT data system. The system used two machine learning techniques which are Artificial Bee Colony algorithm with Support Vector Machine classifier on a San Francisco COVID-19 dataset. The system was evaluated using confusion matrix and had a 95% accuracy, 95% sensitivity, 95% specificity, 97% precision, 96% F1 score, 89% Matthews correlation coefficient for ABC-L-SVM and 97% accuracy, 95% sensitivity, 100% specificity, 100% precision, 97% F1 score, 93.1% Matthews correlation coefficient for ABC-Q-SVM. In conclusion, the system shows that the process of dimensionality reduction utilizing ABC feature extraction techniques can boost the classification production for SVM. It was observed that fetching relevant information from IoT systems before classification is relatively beneficial.

Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases

The recent COVID-19 outbreak has severely affected people around the world. There is a need of an efficient decision making tool to improve awareness about the spread of COVID-19 infections among the common public. An accurate and reliable neural network based tool for predicting confirmed, recovered and death cases of COVID-19 can be very helpful to the health consultants for taking appropriate actions to control the outbreak. This paper proposes a novel Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model for forecasting COVID-19 cases. This NAR-NNTS model is trained with Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR) training algorithms. The performance of the proposed model has been compared by using Root Mean Square Error (RMSE), Mean Square Error (MSE) and correlation co-efficient i.e. R-value. The results show that NAR-NNTS model trained with LM training algorithm performs better than other models for COVID-19 epidemiological data prediction.

Survival study on deep learning techniques for IoT enabled smart healthcare system

Purpose

The paper is to study a review of the employment of deep learning (DL) techniques inside the healthcare sector, together with the highlight of the strength and shortcomings of existing methods together with several research ultimatums. Our study lays the foundation for healthcare professionals and government with present-day inclinations in DL-based data analytics for smart healthcare.

Methods

A deep learning-based technique is designed to extract sensor displacement effects and predict abnormalities for activity recognition via Artificial Intelligence (AI). The presented technique minimizes the vanishing gradient issue of Recurrent Neural Networks (RNN), thereby reducing the time for detecting abnormalities with consideration of temporal and spatial factors. Proposed Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN) introduced.

Results

Experimental results show the feasibility of the proposed method. The results show that the proposed method improves accuracy by 95% and reduces execution time by 18%.

Conclusion

MAR-ERNN performs well in the activity recognition of health status. Collectively, this IoT-enabled smart healthcare system is utilized by enhancing accuracy, and minimizing time and overhead reduction.

New and emerging forms of data and technologies: literature and bibliometric review

With the increased digitalisation of our society, new and emerging forms of data present new values and opportunities for improved data driven multimedia services, or even new solutions for managing future global pandemics (i.e., Disease X). This article conducts a literature review and bibliometric analysis of existing research records on new and emerging forms of multimedia data. The literature review engages with qualitative search of the most prominent journal and conference publications on this topic. The bibliometric analysis engages with statistical software (i.e. R) analysis of Web of Science data records. The results are somewhat unexpected. Despite the special relationship between the US and the UK, there is not much evidence of collaboration in research on this topic. Similarly, despite the negative media publicity on the current relationship between the US and China (and the US sanctions on China), the research on this topic seems to be growing strong. However, it would be interesting to repeat this exercise after a few years and compare the results. It is possible that the effect of the current US sanctions on China has not taken its full effect yet.

Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data

Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data. There was a gradual increase in the positive testing rate up to a first peak rate in July, 2020, then a decrease before another peak around mid-December 2020 to mid-January 2021. The proposed semi-parametric smoothing model provides a data driven estimates for both the positive testing rate and its change. We provide an online R dashboard that can be used to estimate the positive rate in any country of interest based on publicly available data. We believe this is a useful tool for both researchers and policymakers for planning intervention and understanding the COVID-19 spread.

COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans

This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

Background

COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situation culminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. The pandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern, forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, having knowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view to accordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemic to both public health and economic sectors.

Methods

Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory (LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm Convolutional Neural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, South Africa and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developed for 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively.

Results

The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAE and RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of 98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesser MSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively.

Conclusions

Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infection cases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but also in terms of lesser MSE, MAE and RMSE.

An AI model of sonographer’s evaluation+ S-Detect + elastography + clinical information improves the preoperative identification of benign and malignant breast masses

Purpose

The purpose of the study was to build an AI model with selected preoperative clinical features to further improve the accuracy of the assessment of benign and malignant breast nodules.

Methods

Patients who underwent ultrasound, strain elastography, and S-Detect before ultrasound-guided biopsy or surgical excision were enrolled. The diagnosis model was built using a logistic regression model. The diagnostic performances of different models were evaluated and compared.

Results

A total of 179 lesions (101 benign and 78 malignant) were included. The whole dataset consisted of a training set (145 patients) and an independent test set (34 patients). The AI models constructed based on clinical features, ultrasound features, and strain elastography to predict and classify benign and malignant breast nodules had ROC AUCs of 0.87, 0.81, and 0.79 in the test set. The AUCs of the sonographer and S-Detect were 0.75 and 0.82, respectively, in the test set. The AUC of the combined AI model with the best performance was 0.89 in the test set. The combined AI model showed a better specificity of 0.92 than the other models. The sonographer’s assessment showed better sensitivity (0.97 in the test set).

Conclusion

The combined AI model could improve the preoperative identification of benign and malignant breast masses and may reduce unnecessary breast biopsies.

COL11A1 as an novel biomarker for breast cancer with machine learning and immunohistochemistry validation

Machine learning (ML) algorithms were used to identify a novel biological target for breast cancer and explored its relationship with the tumor microenvironment (TME) and patient prognosis. The edgR package identified hub genes associated with overall survival (OS) and prognosis, which were validated using public datasets. Of 149 up-regulated genes identified in tumor tissues, three ML algorithms identified COL11A1 as a hub gene. COL11A1was highly expressed in breast cancer samples and associated with a poor prognosis, and positively correlated with a stromal score (r=0.49, p<0.001) and the ESTIMATE score (r=0.29, p<0.001) in the TME. Furthermore, COL11A1 negatively correlated with B cells, CD4 and CD8 cells, but positively associated with cancer-associated fibroblasts. Forty-three related immune-regulation genes associated with COL11A1 were identified, and a five-gene immune regulation signature was built. Compared with clinical factors, this gene signature was an independent risk factor for prognosis (HR=2.591, 95%CI 1.831–3.668, p=7.7e-08). A nomogram combining the gene signature with clinical variables, showed better predictive performance (C-index=0.776). The model correction prediction curve showed little bias from the ideal curve. COL11A1 is a potential therapeutic target in breast cancer and may be involved in the tumor immune infiltration; its high expression is strongly associated with poor prognosis.

Enhancement of license plate recognition performance using Xception with Mish activation function

The current breakthroughs in the highway research sector have resulted in a greater awareness and focus on the construction of an effective Intelligent Transportation System (ITS). One of the most actively researched areas is Vehicle Licence Plate Recognition (VLPR), concerned with determining the characters contained in a vehicle's Licence Plate (LP). Many existing methods have been used to deal with different environmental complexity factors but are limited to motion deblurring. The aim of our research is to provide an effective and robust solution for recognizing characters present in license plates in complex environmental conditions. Our proposed approach is capable of handling not only the motion-blurred LPs but also recognizing the characters present in different types of low resolution and blurred license plates, illegible vehicle plates, license plates present in different weather and light conditions, and various traffic circumstances, as well as high-speed vehicles. Our research provides a series of different approaches to execute different steps in the character recognition process. The proposed approach presents the concept of Generative Adversarial Networks (GAN) with Discrete Cosine Transform (DCT) Discriminator (DCTGAN), a joint image super resolution and deblurring approach that uses a discrete cosine transform with low computational complexity to remove various types of blur and complexities from licence plates. License Plates (LPs) are detected using the Improved Bernsen Algorithm (IBA) with Connected Component Analysis(CCA). Finally, with the aid of the proposed Xception model with transfer learning, the characters in LPs are recognised. Here we have not used any segmentation technique to split the characters. Four benchmark datasets such as Stanford Cars, FZU Cars, HumAIn 2019 Challenge datasets, and Application-Oriented License Plate (AOLP) dataset, as well as our own collected dataset, were used for the validation of our proposed algorithm. This dataset includes the images of vehicles captured in different lighting and weather conditions such as sunny, rainy, cloudy, blurred, low illumination, foggy, and night. The suggested strategy does better than the current best practices in both numbers and quality.

Time to Recovery of Severely Ill COVID-19 Patients and its Predictors: A Retrospective Cohort Study in Tigray, Ethiopia

Background

COVID-19 is one of the leading causes of morbidity and mortality and is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). A patient infected with SARS-CoV-2 is said to be recovered from the infection following negative test results and when signs and symptoms disappear. Different studies have shown different median recovery time of patients with COVID-19 and it varies across settings and disease status. This study aimed to assess time to recovery and its predictors among severely ill COVID-19 patients in Tigray.

Methods

A total of 139 severely ill COVID-19 patients who were hospitalized between May 7, 2020 and October 28, 2020 were retrospectively analyzed. Cox proportional hazard regression model was fitted to identify the risk factors associated with the time duration to recovery from severe COVID-19 illness.

Results

The median age of the patients was 35 years (IQR, 27–60). Eighty-three (59.7%) patients recovered with a median time of 26 days (95% CI: 23–27). The results from the multivariable analysis showed that the recovery time was lower for severely ill patients who had no underline comorbidity diseases (AHR=2.48, 95% CI: 1.18–5.24), shortness of breath (AHR=2.08, 95% CI: 1.07–3.98) and body weakness (AHR=2.62, 95% CI: 1.20–5.72). Moreover, COVID-19 patients aged younger than 40 years had lower recovery time compared to patients aged 60 and above (AHR=4.09, 95% CI: 1.58–10.61).

Conclusion

The median recovery time of severely ill COVID-19 patients was long, and older age, comorbidity, shortness of breath, and body weakness were significant factors related with the time to recovery among the severely ill COVID-19 patients. Therefore, we recommended that elders and individuals with at least one comorbidity disease have to get due attention to prevent infection by the virus. Moreover, attention should be given in the treatment practice for individuals who had shortness of breath and body weakness symptoms.

Deep learning framework for prediction of infection severity of COVID-19

With the onset of the COVID-19 pandemic, quantifying the condition of positively diagnosed patients is of paramount importance. Chest CT scans can be used to measure the severity of a lung infection and the isolate involvement sites in order to increase awareness of a patient's disease progression. In this work, we developed a deep learning framework for lung infection severity prediction. To this end, we collected a dataset of 232 chest CT scans and involved two public datasets with an additional 59 scans for our model's training and used two external test sets with 21 scans for evaluation. On an input chest Computer Tomography (CT) scan, our framework, in parallel, performs a lung lobe segmentation utilizing a pre-trained model and infection segmentation using three distinct trained SE-ResNet18 based U-Net models, one for each of the axial, coronal, and sagittal views. By having the lobe and infection segmentation masks, we calculate the infection severity percentage in each lobe and classify that percentage into 6 categories of infection severity score using a k-nearest neighbors (k-NN) model. The lobe segmentation model achieved a Dice Similarity Score (DSC) in the range of [0.918, 0.981] for different lung lobes and our infection segmentation models gained DSC scores of 0.7254 and 0.7105 on our two test sets, respectfully. Similarly, two resident radiologists were assigned the same infection segmentation tasks, for which they obtained a DSC score of 0.7281 and 0.6693 on the two test sets. At last, performance on infection severity score over the entire test datasets was calculated, for which the framework's resulted in a Mean Absolute Error (MAE) of 0.505 ± 0.029, while the resident radiologists' was 0.571 ± 0.039.

Clustering-Based Recommendation System for Preliminary Disease Detection

The catastrophic outbreak COVID-19 has brought threat to the society and also placed severe stress on the healthcare systems worldwide. Different segments of society are contributing to their best effort to curb the spread of COVID-19. As a part of this contribution, in this research, a clustering-based recommender system is proposed for early detection of COVID-19 based on the symptoms of an individual. For this, the suspected patient's symptoms are compared with the patient who has already contracted COVID-19 by computing similarity between symptoms. Based on this, the suspected person is classified into either of the three risk categories: high, medium, and low. This is not a confirmed test but only a mechanism to alert the suspected patient. The accuracy of the algorithm is more than 85%.

Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images

Background

Corona Virus Disease 2019 (COVID-19) first appeared in December 2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel coronavirus infection in 2019. COVID-19 is highly infectious and transmissible. By 7 May 2021, the total number of cumulative number of deaths is 3,259,033. In order to diagnose the infected person in time to prevent the spread of the virus, the diagnosis method for COVID-19 is extremely important. To solve the above problems, this paper introduces a Multi-Level Enhanced Sensation module (MLES), and proposes a new convolutional neural network model, MLES-Net, based on this module.

Methods

Attention has the ability to automatically focus on the key points in various information, and Attention can realize parallelism, which can replace some recurrent neural networks to a certain extent and improve the efficiency of the model. We used the correlation between global and local features to generate the attention mask. First, the feature map was divided into multiple groups, and the initial attention mask was obtained by the dot product of each feature group and the feature after the global pooling. Then the attention masks were normalized. At the same time, there were two scaling and translating parameters in each group so that the normalize operation could be restored. Then, the final attention mask was obtained through the sigmoid function, and the feature of each location in the original feature group was scaled. Meanwhile, we use different classifiers on the network models with different network layers.

Results

The network uses three classifiers, FC module (fully connected layer), GAP module (global average pooling layer) and GAPFC module (global average pooling layer and fully connected layer), to improve recognition efficiency. GAPFC as a classifier can obtain the best comprehensive effect by comparing the number of parameters, the amount of calculation and the detection accuracy. The experimental results show that the MLES-Net56-GAPFC achieves the best overall accuracy rate (95.27%) and the best recognition rate for COVID-19 category (100%).

Conclusions

MLES-Net56-GAPFC has good classification ability for the characteristics of high similarity between categories of COVID-19 X-Ray images and low intra-category variability. Considering the factors such as accuracy rate, number of network model parameters and calculation amount, we believe that the MLES-Net56-GAPFC network model has better practicability.

Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations

Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.

Predicting Corona Virus Affected Patients Using Supervised Machine Learning

The world is infected from the deadliest pandemic disease humankind has ever seen. Several medical practitioners have been encountered with the corona virus and are constantly losing their lives in the fight. Hence, the main objective of this research work is to characterize the clinical features of the patients and construct a novel dataset for machine learning to classify them accurately prior to treatment. The positive patients can be identified on many characteristics and the principle data for this research is considered on the basis of the exploratory analysis done on the various risk factors that is also associated with the mortality in the hospitals. As an outcome, this article presents a supervised machine learning model incorporating the insights, symptoms and classification of the corona virus infected person. The proposed model and the dataset are tested against six well known classifiers on various levels of cross folding and percentage splits. The proposed dataset is also tested against the actual patient records and was found that the model accurately categorizes them prior to their treatment. The experimental results for proposed techniques showed higher performance and better accuracy further creating an impact on then identification of corona virus patients.

IoT Based Smart Assist System to Monitor Entertainment Spots Occupancy and COVID 19 Screening During the Pandemic

The greatest threat to the word in recent days is the spread of COVID 19 virus throughout the world. To tackle this problem government of India has implemented various restrictions to be followed to stop the spread of the COVID 19 virus. But most of the time general public forget their responsibilities and don't follow these restrictions, especially in situations like when their favourite hero's movie releases in the theatre, and in spending time in hotels, malls and in other entertainment places in spite of governments occupancy restrictions in those places. In order to address this problem we propose an IoT based Smart System for monitoring the occupancy in such entertainment spots and screen the public entry if they dint follow the protocols such as if they dint wear mask or if they have body temperature. This proposed system is implemented on a Raspberry Pi 3B+ processor which runs on a Broadcom processor. For monitoring the occupancy and screen the visitors for mask, we use a Passive Infrared Sensors and Pi camera to count the person entering into the premises. And we use a MLX90614 Infrared temperature sensor for screening the public entry with high temperature. The complete system is implemented using python programming and the details will be uploaded to cloud, authorities can monitor this from a remote place so that the spread of COVID 19 can be restricted in pubic entertainment spots.

Sports Economic Mining Algorithm Based on Association Analysis and Big Data Model

With the implementation of national strategies such as sports power and national fitness, the sports economy has become an important element of high-quality national development, and the demand for sports economy and management talents is greatly increased. Particularly in the new area with big data as the typical feature, the teaching content, teaching method, and teaching mode of sports economics and management majors have put forward new requirements. The continuous progress of storage and network technology has prompted the generation of massive multisource spatiotemporal data in various fields. The advantage of association analysis algorithms is that they are easy to code and implement. The relationships found by association analysis can take two forms: frequent itemsets or association rules. We use correlation analysis methods to perform correlation learning between sports economy and related big data and thus improve the development of sports economy. Mining and analyzing the relevant big data can precisely reveal the problems of sports economic development and can realize the fine management of sports, thus contributing to the healthy development of sports. Mastering the skills of acquiring, analyzing, and applying big data is the core content of sports economic analysis. The sports economy has refined and intelligent management means, and its adoption of virtual reality reflects the current situation and development trend of the sports business, which further highlights the status and role of multisource big data in the sports economy. Based on these, this paper proposed a sports economy mining algorithm in view of the correlation analysis and big data model. Then, we verified the effectiveness of the model through experiments, which laid the foundation for the development of the sports economy.

ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.

Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey

Graphical-design-based symptomatic techniques in pandemics perform a quintessential purpose in screening hit causes that comparatively render better outcomes amongst the principal radioscopy mechanisms in recognizing and diagnosing COVID-19 cases. The deep learning paradigm has been applied vastly to investigate radiographic images such as Chest X-Rays (CXR) and CT scan images. These radiographic images are rich in information such as patterns and clusters like structures, which are evident in conformance and detection of COVID-19 like pandemics. This paper aims to comprehensively study and analyze detection methodology based on Deep learning techniques for COVID-19 diagnosis. Deep learning technology is a good, practical, and affordable modality that can be deemed a reliable technique for adequately diagnosing the COVID-19 virus. Furthermore, the research determines the potential to enhance image character through artificial intelligence and distinguishes the most inexpensive and most trustworthy imaging method to anticipate dreadful viruses. This paper further discusses the cost-effectiveness of the surveyed methods for detecting COVID-19, in contrast with the other methods. Several finance-related aspects of COVID-19 detection effectiveness of different methods used for COVID-19 detection have been discussed. Overall, this study presents an overview of COVID-19 detection using deep learning methods and their cost-effectiveness and financial implications from the perspective of insurance claim settlement.

Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review

The deadly coronavirus (COVID-19) is one of the dangerous diseases affecting the entire world and is fastly spreading disease. This spread can be reduced by detecting and quarantining the patients at an earlier stage. The most common diagnostic tool for detecting the coronavirus is the Reverse transcription-polymerase chain reaction (RT-PCR) test which is time-consuming and also needs more equipment and manpower. Furthermore, many countries had a deficit of RTPCR kits. This is why it is exceptionally very crucial to develop artificial intelligence (AI) techniques to detect the outbreak of coronavirus. This motivated many researchers to involve deep-learning methods using X-ray images for more decisive analysis. Thus, this paper outlines many papers that used traditional and pre-trained deep learning methods that are newly developed to reduce the spread of COVID-19 disease. Specifically, advanced deep learning methods play a critical role in extracting the features from the chest X-ray images. These features are then used to classify whether the patient is affected with coronavirus or not. Besides, this paper shows that deep learning techniques have probable applications in the medical field.

The State of the Art of Data Mining Algorithms for Predicting the COVID-19 Pandemic

Current computer systems are accumulating huge amounts of information in several application domains. The outbreak of COVID-19 has increased rekindled interest in the use of data mining techniques for the analysis of factors that are related to the emergence of an epidemic. Data mining techniques are being used in the analysis and interpretation of information, which helps in the discovery of patterns, planning of isolation policies, and even predicting the speed of proliferation of contagion in a viral disease such as COVID-19. This research provides a comprehensive study of various data mining algorithms that are used in conjunction with epidemiological prediction models. The document considers that there is an opportunity to improve or develop tools that offer an accurate prognosis in the management of viral diseases through the use of data mining tools, based on a comparative study of 35 research papers.

Statistical analysis of blood characteristics of COVID-19 patients and their survival or death prediction using machine learning algorithms

This study’s main purpose is to provide helpful information using blood samples from COVID-19 patients as a non-medical approach for helping healthcare systems during the pandemic. Also, this paper aims to evaluate machine learning algorithms for predicting the survival or death of COVID-19 patients. We use a blood sample dataset of 306 infected patients in Wuhan, China, compiled by Tangji Hospital. The dataset consists of blood’s clinical indicators and information about whether patients are recovering or not. The used methods include K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), bagging classifier (BC), and adaptive boosting (AdaBoost). We compare the performance of machine learning algorithms using statistical hypothesis testing. The results show that the most critical feature is age, and there is a high correlation between LD and CRP, and leukocytes and CRP. Furthermore, RF, SVM, DT, AdaBoost, DT, and KNN outperform other machine learning algorithms in predicting the survival or death of COVID-19 patients.

A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system

Starting from December 2019, the COVID-19 pandemic has globally strained medical resources and caused significant mortality. It is commonly recognized that the severity of SARS-CoV-2 disease depends on both the comorbidity and the state of the patient's immune system, which is reflected in several biomarkers. The development of early diagnosis and disease severity prediction methods can reduce the burden on the health care system and increase the effectiveness of treatment and rehabilitation of patients with severe cases. This study aims to develop and validate an ensemble machine-learning model based on clinical and immunological features for severity risk assessment and post-COVID rehabilitation duration for SARS-CoV-2 patients. The dataset consisting of 35 features and 122 instances was collected from Lviv regional rehabilitation center. The dataset contains age, gender, weight, height, BMI, CAT, 6-minute walking test, pulse, external respiration function, oxygen saturation, and 15 immunological markers used to predict the relationship between disease duration and biomarkers using the machine learning approach. The predictions are assessed through an area under the receiver-operating curve, classification accuracy, precision, recall, and F1 score performance metrics. A new hybrid ensemble feature selection model for a post-COVID prediction system is proposed as an automatic feature cut-off rank identifier. A three-layer high accuracy stacking ensemble classification model for intelligent analysis of short medical datasets is presented. Together with weak predictors, the associative rules allowed improving the classification quality. The proposed ensemble allows using a random forest model as an aggregator for weak repressors' results generalization. The performance of the three-layer stacking ensemble classification model (AUC 0.978; CA 0.920; F1 score 0.921; precision 0.924; recall 0.920) was higher than five machine learning models, viz. tree algorithm with forward pruning; Naïve Bayes classifier; support vector machine with RBF kernel; logistic regression, and a calibrated learner with sigmoid function and decision threshold optimization. Aging-related biomarkers, viz. CD3+, CD4+, CD8+, CD22+ were examined to predict post-COVID rehabilitation duration. The best accuracy was reached in the case of the support vector machine with the linear kernel (MAPE = 0.0787) and random forest classifier (RMSE = 1.822). The proposed three-layer stacking ensemble classification model predicted SARS-CoV-2 disease severity based on the cytokines and physiological biomarkers. The results point out that changes in studied biomarkers associated with the severity of the disease can be used to monitor the severity and forecast the rehabilitation duration.

Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach

The dynamical nature of COVID-19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID-19 cases based on past infections, (b) predict current COVID-19 cases using PM2.5, temperature, and humidity data, using four different machine learning classifiers (Decision Tree, K-nearest neighbor, Support Vector Machine, and Random Forest). Based on RMSE values, k-nearest neighbor and support vector machine algorithms were found to be the best for predicting future incidences of COVID-19 based on past histories. From the RMSE values obtained, temperature was found to be the best predictor for number of COVID-19 cases, followed by relative humidity. Decision tree models was found to perform poorly in the prediction of COVID-19 cases considering particulate matter and atmospheric parameters as predictors. Our results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control.

A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis

Liver segmentation and recognition from computed tomography (CT) images is a warm topic in image processing which is helpful for doctors and practitioners. Currently, many deep learning methods are used for liver segmentation that takes a long time to train the model which makes this task challenging and limited to larger hardware resources. In this research, we proposed a very lightweight convolutional neural network (CNN) to extract the liver region from CT scan images. The suggested CNN algorithm consists of 3 convolutional and 2 fully connected layers, where softmax is used to discriminate the liver from background. Random Gaussian distribution is used for weight initialization which achieved a distance-preserving-embedding of the information. The proposed network is known as Ga-CNN (Gaussian-weight initialization of CNN). General experiments are performed on three benchmark datasets including MICCAI SLiver’07, 3Dircadb01, and LiTS17. Experimental results show that the proposed method performed well on each benchmark dataset.

Prognosis patients with COVID-19 using deep learning

Background

The coronavirus (COVID-19) is a novel pandemic and recently we do not have enough knowledge about the virus behaviour and key performance indicators (KPIs) to assess the mortality risk forecast. However, using a lot of complex and expensive biomarkers could be impossible for many low budget hospitals. Timely identification of the risk of mortality of COVID-19 patients (RMCPs) is essential to improve hospitals' management systems and resource allocation standards.

Methods

For the mortality risk prediction, this research work proposes a COVID-19 mortality risk calculator based on a deep learning (DL) model and based on a dataset provided by the HM Hospitals Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. To evaluate the proposed methods, an over-sampling Synthetic Minority TEchnique (SMOTE) and data imputation approaches are introduced which is based on the K-nearest neighbour.

Results

A total of 1,503 seriously ill COVID-19 patients having a median age of 70 years old are comprised in the research work, with 927 (61.7%) males and 576 (38.3%) females. A total of 48 features are considered to evaluate the proposed method, and the following results are achieved. It includes the following values i.e., area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision (MPCD) 0.93.

Conclusion

The results show that the proposed method is significantly best for the mortality risk prediction of patients with COVID-19 infection. The MPCD score shows that the proposed DL outperforms on every dataset when evaluating even with an over-sampling technique. The benefits of the data imputation algorithm for unavailable biomarker data are also evaluated. Based on the results, the proposed scheme could be an appropriate tool for critically ill Covid-19 patients to assess the risk of mortality and prognosis.

Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis

A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis is ideal for researchers to understand the research trend and future directions. Influential articles, institutes, countries, authors, and commonly used keywords were analyzed to grasp a comprehensive view on our topic, alerts in CDSS. Articles published between 2011 and 2021 were extracted from the Web of Science database. There were 728 articles included for bibliometric analysis, among which 24 papers were selected for content analysis. Our analysis shows that the research direction has shifted from patient safety to system utility, implying the importance of alert usability to be clinically impactful. Finally, we conclude with future research directions such as the optimization of alert mechanisms and comprehensiveness to enhance alert appropriateness and to reduce alert fatigue.

Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning

Coronavirus disease 2019 (COVID-19) has resulted in approximately 5 million deaths around the world with unprecedented consequences in people’s daily routines and in the global economy. Despite vast increases in time and money spent on COVID-19-related research, there is still limited information about the factors at the country level that affected COVID-19 transmission and fatality in EU. The paper focuses on the identification of these risk factors using a machine learning (ML) predictive pipeline and an associated explainability analysis. To achieve this, a hybrid dataset was created employing publicly available sources comprising heterogeneous parameters from the majority of EU countries, e.g., mobility measures, policy responses, vaccinations, and demographics/generic country-level parameters. Data pre-processing and data exploration techniques were initially applied to normalize the available data and decrease the feature dimensionality of the data problem considered. Then, a linear ε-Support Vector Machine (ε-SVM) model was employed to implement the regression task of predicting the number of deaths for each one of the three first pandemic waves (with mean square error of 0.027 for wave 1 and less than 0.02 for waves 2 and 3). Post hoc explainability analysis was finally applied to uncover the rationale behind the decision-making mechanisms of the ML pipeline and thus enhance our understanding with respect to the contribution of the selected country-level parameters to the prediction of COVID-19 deaths in EU.

A deep learning-driven low-power, accurate, and portable platform for rapid detection of COVID-19 using reverse-transcription loop-mediated isothermal amplification

This paper presents a deep learning-driven portable, accurate, low-cost, and easy-to-use device to perform Reverse-Transcription Loop-Mediated Isothermal Amplification (RT-LAMP) to facilitate rapid detection of COVID-19. The 3D-printed device—powered using only a 5 Volt AC-DC adapter—can perform 16 simultaneous RT-LAMP reactions and can be used multiple times. Moreover, the experimental protocol is devised to obviate the need for separate, expensive equipment for RNA extraction in addition to eliminating sample evaporation. The entire process from sample preparation to the qualitative assessment of the LAMP amplification takes only 45 min (10 min for pre-heating and 35 min for RT-LAMP reactions). The completion of the amplification reaction yields a fuchsia color for the negative samples and either a yellow or orange color for the positive samples, based on a pH indicator dye. The device is coupled with a novel deep learning system that automatically analyzes the amplification results and pays attention to the pH indicator dye to screen the COVID-19 subjects. The proposed device has been rigorously tested on 250 RT-LAMP clinical samples, where it achieved an overall specificity and sensitivity of 0.9666 and 0.9722, respectively with a recall of 0.9892 for C_t < 30. Also, the proposed system can be widely used as an accurate, sensitive, rapid, and portable tool to detect COVID–19 in settings where access to a lab is difficult, or the results are urgently required.

Review on COVID-19 diagnosis models based on machine learning and deep learning approaches

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

Modelling Covid-19 infections in Zambia using data mining techniques

The outbreak of Covid-19 pandemic has been declared a global health crisis by the World Health Organization since its emergence. Several researchers have proposed a number of techniques to understand how the pandemic affects the populations. Reported among these techniques are data mining models which have been successfully applied in a wide range of situations before the advent of Covid-19 pandemic. In this work, the researchers have applied a number of existing data mining methods (classifiers) available in the Waikato Environment for Knowledge Analysis (WEKA) machine learning library. WEKA was used to gain a better understanding on how the epidemic spread within Zambia. The classifiers used are J48 decision tree, Multilayer Perceptron and Naïve Bayes among others. The predictions of these techniques are compared against simpler classifiers and those reported in related works.