Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance

COVID-19 activity screening by a smart-data-driven multi-band voice analysis

COVID-19 is a disease caused by the new coronavirus SARS-COV-2 which can lead to severe respiratory infections. Since its first detection it caused more than six million worldwide deaths. COVID-19 diagnosis non-invasive and low-cost methods with faster and accurate results are still needed for a fast disease control. In this research, 3 different signal analyses have been applied (per broadband, per sub-bands and per broadband & sub-bands) to Cough, Breathing & Speech signals of Coswara dataset to extract non-linear patterns (Energy, Entropies, Correlation Dimension, Detrended Fluctuation Analysis, Lyapunov Exponent & Fractal Dimensions) for feeding a XGBoost classifier to discriminate COVID-19 activity on its different stages. Classification accuracies ranged between 83.33% and 98.46% have been achieved, surpassing the state-of-art methods in some comparisons. It should be empathized the 98.46% of accuracy reached on pair Healthy Controls vs all COVID-19 stages. The results shows that the method may be adequate for COVID-19 diagnosis screening assistance.

Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data

Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.

Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data

BACKGROUND

Despite the globally reducing hospitalization rates and the much lower risks of Covid-19 mortality, accurate diagnosis of the infection stage and prediction of outcomes are clinically of interest. Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients' clinical data.

METHODS

We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers fed with patients' clinical data only. A relatively large clinical dataset from 380 Covid-19 diagnosed patients was used to train/test the models.

RESULTS

Results show that the 3D Video Swin Transformers fed with the fusion datasets of 64 sectional CT scans + 67 clinical labels outperformed all other approaches for predicting outcomes in Covid-19-infected patients amongst all techniques (i.e., TPR = 0.95, FPR = 0.40, F0.5 score = 0.82, AUC = 0.77, Kappa = 0.6).

CONCLUSIONS

We demonstrate how the utility of our proposed novel 3D data fusion approach through concatenating CT scan images with patients' clinical data can remarkably improve the performance of the models in predicting Covid-19 infection outcomes.

SIGNIFICANCE

Findings indicate possibilities of predicting the severity of outcome using patients' CT images and clinical data collected at the time of admission to hospital.

Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN

The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.

Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification

Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneumonia), COVID-19 Image Dataset (COVID-19, Viral Pneumonia, Normal), and COVID-19 Radiography Database. We achieved testing and validation accuracy of 90.6% on a relatively small dataset of 6432 images. Our proposed model improved accuracy and reduced time complexity, making it more practical for medical diagnosis. Our approach offers a more interpretable and transparent AI model that can enhance the accuracy and efficiency of medical diagnosis.

Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy

BACKGROUND

Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awareness issues, poor healthcare, and late detection. To curb the growing threats from liver disease, early detection is critical to help reduce the risks and improve treatment outcome. Emerging technologies such as machine learning, as shown in this study, could be deployed to assist in enhancing its prediction and treatment.

AIM

To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection, diagnosis, and reduction of risks and mortality associated with the disease.

METHODS

The dataset used in this study consisted of 416 people with liver problems and 167 with no such history. The data were collected from the state of Andhra Pradesh, India, through https://www.kaggle.com/datasets/uciml/indian-liver-patient-records. The population was divided into two sets depending on the disease state of the patient. This binary information was recorded in the attribute "is_patient".

RESULTS

The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36% and 73.24%, respectively, which was much better than the conventional method. The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis (scarring) and to enhance the survival of patients. The study showed the potential of machine learning in health care, especially as it concerns disease prediction and monitoring.

CONCLUSION

This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease. However, relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential.

Preparation of surface-functionalized electrospun PVA nanowebs for potential remedy for SARS-CoV-2

Infections with coronaviruses remain a burden that is negatively affecting human life. The use of metal oxides to prevent and control the spread of severe acute respiratory syndrome coronavirus (SARS-CoV-2) has been widely studied. However, the use of metal oxides in masks to enhance the performances of barrier face coverings in trapping and neutralizing SARS-CoV-2 remained unexplored. In the present study, we explore the possibility of developing surface functional PVA/ZnO electrospun nanowebs to be used as a component of multilayer barrier face coverings. Polyvinyl alcohol (PVA) and zinc acetate (ZnA) nanowebs were electrospun as precursor samples. After calcination at 400 degrees centigrade under a controlled atmosphere of nitrogen gas, product nanowebs containing ZnO (PVA/ZnO) were obtained. The presence of ZnO was determined using an attenuated total reflectance Fourier Transform Infrared (FT-IR) spectrometer. This study inspired the possibility of developing surface-functional materials to produce enhanced performance masks against the spread of SARS-CoV-2.

Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications

Nowadays, the concept of smart sustainable governance is wrapped around basic principles such as: (i) transparency, (ii) accountability, (iii) stakeholders’ involvement, and iv) citizens’ participation. It is through these principles that are influenced by information and communication technologies (ICT), Internet of Things (IoT), and artificial intelligence, that the practices employed by citizens and their interaction with electronic government (e-government) are diversified. Previously, the misleading concepts of the smart city implied only the objective of the local level or public officials to utilize technology. However, the recent European experience and research studies have led to a more comprehensive notion that refers to the search for intelligent solutions which allow modern sustainable cities to enhance the quality of services provided to citizens and to improve the management of urban mobility. The smart city is based on the usage of connected sensors, data management, and analytics platforms to improve the quality and functioning of built-environment systems. The aim of this paper is to understand the effects of the pandemic on smart cities and to accentuate major exercises that can be learned for post-COVID sustainable urban management and patterns. The lessons and implications outlined in this paper can be used to enforce social distancing community measures in an effective and timely way, and to optimize the use of resources in smart and sustainable cities in critical situations. The paper offers a conceptual overview and serves as a stepping-stone to extensive research and the deployment of sustainable smart city platforms and intelligent transportation systems (a sub-area of smart city applications) after the COVID-19 pandemic using a case study from Russia. Overall, our results demonstrate that the COVID-19 crisis encompasses an excellent opportunity for urban planners and policy makers to take transformative actions towards creating cities that are more intelligent and sustainable.

Digital government transformation in turbulent times: Responses, challenges, and future direction

We are living in turbulent times, with the threats of COVID-19 and related social conflicts. Digital transformation is not an option but a necessity for governments to respond to these crises. It has become imperative for governments worldwide to enhance their capacity to strategically use emerging digital technologies and develop innovative digital public services to confront and overcome the pandemic. With the rapid development of digital technologies, digital government transformation (DGT) has been legitimated in response to the pandemic, contributing to innovative efficacy, but it also has created a set of challenges, dilemmas, paradoxes, and ambiguities. This special issue's primary motive is to comprehensively discuss the promises and challenges DGT presents. It focuses on the nature of the problems and the dilemmatic situation in which to use the technologies. Furthermore, it covers government capacity and policy implications for managerial and institutional reforms to respond to the threats and the uncertainty caused by disruptive digitalization in many countries. To stimulate discussion of the theme of this special issue, this editorial note provides an overview of previous literature on DGT as a controlling measure of the pandemic and the future direction of research and practice on DGT.

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.

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body's respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient's computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with "COVID" and "Non-COVID." The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

Internet of Things use case applications for COVID-19

The Internet of Things (IoT) is a technology built upon various physical objects equipped with different types of sensors, which are connected together using communication methods. These devices have been applied to several domains, especially healthcare. In addition to the numerous benefits that IoT has demonstrated in healthcare, this technology is being adopted for combating the recent COVID-19 pandemic. The key role of IoT in COVID-19 could be classified into five major tasks: Monitoring, Diagnosing, Tracing, Disinfecting, and Vaccinating. This chapter reviews the state-of-art applications of IoT based on these tasks in order to better mitigate this virus. Additionally, potential areas for applying IoT systems to fight against COVID-19 or even future pandemics will be demonstrated.

Deep Learning Models for Multiple Face Mask Detection under a Complex Big Data Environment

The Covid-19 (coronavirus) pandemic creates a worldwide health crisis. According to the WHO, the effective protection system is wearing a face mask in public places. Many studies proved that carrying a face mask is also one of the precautions to decrease the possibility of viral transmission. Strict monitoring of face mask being worn by people is now enforced in many countries. Manual observation and monitoring is quite tedious. Hence, automated systems have been researched using well-kwown face mask detection methods. However, this research paper, deals with some deep learning models which can be effectively used to detect multiple face masks in a crowded environment when the amount of incoming data from sensors is huge or in otherwise stated to a Big data problem. Hence, standalone face detection models are not quite suited. Deep learning models are required in such Big data scenario which forms the essence of this study.

Towards applying internet of things and machine learning for the risk prediction of COVID-19 in pandemic situation using Naive Bayes classifier for improving accuracy

Infections such as COVID-19 are affecting the entire world and measures such as social distancing can be done so that the contact among people is reduced. IoT devices usage keeps on increasing every day thereby connecting the environments physically. Among the current technologies, machine learning can be employed along with IoT devices. Predicting the risk related with COVID-19, a novel method employing machine learning is proposed. Random forest and Naive Bayes classifier are used for the prediction from the data collected with the help of sensors. Groups of people are recognized and the disease impact can be reduced for the particular group with more population. The accuracy of RF is 97% and for NB it is 99%.

Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction

Since the beginning of the COVID-19 pandemic, 195 million people have been infected and 4.2 million have died from the disease or its side effects. Physicians, healthcare scientists and medical staff continuously try to deal with overloaded hospital admissions, while in parallel, they try to identify meaningful correlations between the severity of infected patients with their symptoms, comorbidities and biomarkers. Artificial intelligence (AI) and machine learning (ML) have been used recently in many areas related to COVID-19 healthcare. The main goal is to manage effectively the wide variety of issues related to COVID-19 and its consequences. The existing applications of ML to COVID-19 healthcare are based on supervised classifications which require a labeled training dataset, serving as reference point for learning, as well as predefined classes. However, the existing knowledge about COVID-19 and its consequences is still not solid and the points of common agreement among different scientific communities are still unclear. Therefore, this study aimed to follow an unsupervised clustering approach, where prior knowledge is not required (tabula rasa). More specifically, 268 hospitalized patients at the First Propaedeutic Department of Internal Medicine of AHEPA University Hospital of Thessaloniki were assessed in terms of 40 clinical variables (numerical and categorical), leading to a high-dimensionality dataset. Dimensionality reduction was performed by applying a principal component analysis (PCA) on the numerical part of the dataset and a multiple correspondence analysis (MCA) on the categorical part of the dataset. Then, the Bayesian information criterion (BIC) was applied to Gaussian mixture models (GMM) in order to identify the optimal number of clusters under which the best grouping of patients occurs. The proposed methodology identified four clusters of patients with similar clinical characteristics. The analysis revealed a cluster of asymptomatic patients that resulted in death at a rate of 23.8%. This striking result forces us to reconsider the relationship between the severity of COVID-19 clinical symptoms and the patient's mortality.

Stacking Ensemble-Based Intelligent Machine Learning Model for Predicting Post-COVID-19 Complications

The recent outbreak of novel coronavirus disease (COVID-19) has resulted in healthcare crises across the globe. Moreover, the persistent and prolonged complications of post-COVID-19 or long COVID are also putting extreme pressure on hospital authorities due to the constrained healthcare resources. Out of many long-lasting post-COVID-19 complications, heart disease has been realized as the most common among COVID-19 survivors. The motivation behind this research is the limited availability of the post-COVID-19 dataset. In the current research, data related to post-COVID complications are collected by personally contacting the previously infected COVID-19 patients. The dataset is preprocessed to deal with missing values followed by oversampling to generate numerous instances, and model training. A binary classifier based on a stacking ensemble is modeled with deep neural networks for the prediction of heart diseases, post-COVID-19 infection. The proposed model is validated against other baseline techniques, such as decision trees, random forest, support vector machines, and artificial neural networks. Results show that the proposed technique outperforms other baseline techniques and achieves the highest accuracy of 93.23%. Moreover, the results of specificity (95.74%), precision (95.24%), and recall (92.05%) also prove the utility of the adopted approach in comparison to other techniques for the prediction of heart diseases.

Machine learning predictions of COVID-19 second wave end-times in Indian states

The estimate of the remaining time of an ongoing wave of epidemic spreading is a critical issue. Due to the variations of a wide range of parameters in an epidemic, for simple models such as Susceptible-Infected-Removed (SIR) model, it is difficult to estimate such a time scale. On the other hand, multidimensional data with a large set attributes are precisely what one can use in statistical learning algorithms to make predictions. Here we show, how the predictability of the SIR model changes with various parameters using a supervised learning algorithm. We then estimate the condition in which the model gives the least error in predicting the duration of the first wave of the COVID-19 pandemic in different states in India. Finally, we use the SIR model with the above mentioned optimal conditions to generate a training data set and use it in the supervised learning algorithm to estimate the end-time of the ongoing second wave of the pandemic in different states in India.

Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

The blood donation process is essential for health systems. Therefore, the ability to predict donor flow has become relevant for hospitals. Although it is possible to predict this behaviour intention from donor questionnaires, the need to reduce social contact in pandemic settings leads to decreasing the extension of these surveys with the minimum loss of predictivity. In this context, this study aims to predict the intention to give blood again, among donors, based on a limited number of attributes. This research uses data science and learning concepts based on symmetry in a particular classification to predict blood donation intent. We carried out a face-to-face survey of Chilean donors based on the Theory of Planned Behaviour. These data, including control variables, were analysed using the decision tree technique. The results indicate that it is possible to predict the intention to donate blood again with an accuracy of 84.17% and minimal variables. The added scientific value of this article is to propose a more simplified way of measuring a multi-determined social phenomenon, such as the intention to donate blood again and the application of the decision tree technique to achieve this simplification, thereby contributing to the field of data science.

The worldwide methods of artificial intelligence for detection and diagnosis of COVID-19

This chapter aims to present and review the early and recent initiatives, researches, and applications of artificial intelligence (AI) and its methods on the detection and diagnosis of COVID-19 virus. Common methods of AI will be presented on these applications including the traditional work on artificial neural networks and the recent approaches on machine learning and deep learning. In this chapter, a survey on many examples of the application of the AI methods on the detection and diagnosis of COVID-19 will be highlighted including the early trials in China and the recent researches in other regions, beside most trials in all other regions of the World including Asia, North America, Europe, and Africa. This chapter will also show many comparisons of the explained approaches and trials based on methods, type of applications, and regions. Brief view of the future and expected applications and trends of AI in the area of detection and diagnosis of viruses and especially the COVID-19 are explained and discussed at the end of the chapter.