Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Using data analytics for telehealth utilization: A case study in Arkansas.

INTRODUCTION: Many patients used telehealth services during the COVID-19 pandemic. In this study, we evaluate how different factors have affected telehealth utilization in recent years. Decision makers at the federal and state levels can use the results of this study to inform their healthcare-related policy decisions. METHODS: We implemented data analytics techniques to determine the factors that explain the use of telehealth by developing a case study using data from Arkansas. Specifically, we built a random forest regression model which helps us identify the important factors in telehealth utilization. We evaluated how each factor impacts the number of telehealth patients in Arkansas counties. RESULTS: Of the 11 factors evaluated, five are demographic, and six are socioeconomic factors. Socioeconomic factors are relatively easier to influence in the short term. Based on our results, broadband subscription is the most important socioeconomic factor and population density is the most important demographic factor. These two factors were followed by education level, computer use, and disability in terms of their importance as it relates to telehealth use. DISCUSSION: Based on studies in the literature, telehealth has the potential to improve healthcare services by improving doctor utilization, reducing direct and indirect waiting times, and reducing costs. Thus, federal and state decision makers can influence the utilization of telehealth in specific locations by focusing on important factors. For example, investments can be made to increase broadband subscriptions, education levels, and computer use in targeted locations.

Using Machine Learning to Predict Business Survival in COVID-19

The global recession due to the pandemic has knocked the business landscape and brought the world to its knees. There were a number of renowned companies that made the headlines for being the top industry hard hits. Nonetheless, there were businesses that survived this pandemic and navigated the COVID complexities so effectively that it tipped the scales in their favor. We attempt to study the factors that helped these businesses masterfully work their way through the conundrums of coronavirus pandemic. We first build a dataset that entailed information pertinent to businesses and relevant COVID-related information that was sourced from Yelp and other platforms. We used a variety of classifiers to make predictions about the survival of these businesses followed by that after assessing their performance through varied methods. The model efficiency was classified based on several rating techniques to evaluate both underperforming and profitable businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Evaluating Time Influence over Performance of Machine-Learning-Based Diagnosis: A Case Study of COVID-19 Pandemic in Brazil.

Efficiently recognising severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) symptoms enables a quick and accurate diagnosis to be made, and helps in mitigating the spread of the coronavirus disease 2019. However, the emergence of new variants has caused constant changes in the symptoms associate with COVID-19. These constant changes directly impact the performance of machine-learning-based diagnose. In this context, considering the impact of these changes in symptoms over time is necessary for accurate diagnoses. Thus, in this study, we propose a machine-learning-based approach for diagnosing COVID-19 that considers the importance of time in model predictions. Our approach analyses the performance of XGBoost using two different time-based strategies for model training: month-to-month and accumulated strategies. The model was evaluated using known metrics: accuracy, precision, and recall. Furthermore, to explain the impact of feature changes on model prediction, feature importance was measured using the SHAP technique, an XAI technique. We obtained very interesting results: considering time when creating a COVID-19 diagnostic prediction model is advantageous.

Using Machine Learning to Predict Business Survival in COVID-19

The global recession due to the pandemic has knocked the business landscape and brought the world to its knees. There were a number of renowned companies that made the headlines for being the top industry hard hits. Nonetheless, there were businesses that survived this pandemic and navigated the COVID complexities so effectively that it tipped the scales in their favor. We attempt to study the factors that helped these businesses masterfully work their way through the conundrums of coronavirus pandemic. We first build a dataset that entailed information pertinent to businesses and relevant COVID-related information that was sourced from Yelp and other platforms. We used a variety of classifiers to make predictions about the survival of these businesses followed by that after assessing their performance through varied methods. The model efficiency was classified based on several rating techniques to evaluate both underperforming and profitable businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis of changes in geographical factors affecting sales in commercial alleys after COVID-19 using machine learning techniques.

Social restrictions, such as social distancing and self-isolation, imposed owing to the coronavirus disease-19 (COVID-19) pandemic have resulted in a decreased demand of commodities and manufactured products. However, the factors influencing sales in commercial districts in the pre- and post-COVID-19 periods have not yet been fully understood. Thus, this study uses machine learning techniques to identify the changes in important geographical factors among both periods that have affected sales in commercial alleys. It was discovered that, in the post-COVID-19 period, the number of pharmacies, age groups of the working population, average monthly income, and number of families living in apartments priced higher than $600k in the catchment areas had relatively high importance after COVID-19 in the prediction of a high level of sales. Moreover, the percentage of deciduous forests appeared to be a important factor in the post-COVID-19 period. As the average monthly income and worker population in their 60s and numbers of pharmacies and banks increased after the pandemic, sales in commercial alleys also increased. The survival of commercial alleys has become a critical social problem in the post-COVID-19 era; therefore, this study is meaningful in that it suggests a policy direction that could contribute to the revitalization of commercial alley sales in the future and boost the local economy.

Correlation Analysis of Usage Pattern in Home Appliance with Boosting Algorithm

Energy consumption in the home increases recently due to the extremely hot or cold weather. Because of COVID 19, many people stay in the home and energy consumption in the home is increasing very much. Moreover, many homes are using new electric home appliances such as dishwasher or washer dryer which consumes much electric energy for a long duration. To reduce electric energy consumption and use energy more efficiently, the usage pattern of the home appliance should be analyzed. In the paper, we propose a pattern analysis method of the home appliance using Boosting technique. Boosting method is a sort of ensemble machine learning algorithm and is based on the decision tree. The correlation between home appliance usage can be analyzed with the result of feature importance in boosting algorithm. To verify the method, we analyzed the electric usage record in the UK with boosting algorithm. © 2022 IEEE.

Geospatial Features Influencing the Formation of COVID-19 Clusters

Machine Learning methods have been used to combat COVID-19 since the pandemic has started in year 2020. In this regard, most studies have focused on detecting and identifying the characteristics of SARS-CoV-2, especially via image processing. Some studies have applied machine learning for contact tracing to minimise the transmission of COVID-19 cases. Limited work has, however, reported on how geospatial features have an influence on the transmission of COVID-19 and formation of clusters at local scale. Therefore, this paper has aimed to study the importance of geospatial features that had resorted to COVID-19 cluster formation in Kuala Lumpur, Malaysia in year 2021. Several datasets were used in this work, which have included the address details of confirmed positive COVID-19 cases and the details of nearby residential areas and Points of Interest (POI) located within the federal territory of Kuala Lumpur. The datasets were pre-processed and transformed into an analytical dataset for conducting empirical investigations. Various feature selection methods were applied, including the Boruta Algorithm, Chi-square (Chi2) Test, Extra Trees Classifier (ETC), Recursive Feature Elimination (RFE) method, and Deep Learning Autoencoder (DLA). Detailed investigations on the top-n features were performed to elicit a set of optimal features. Subsequently, several machine learning models were trained using the optimal features, including Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC), and Extreme Gradient Boosting (XGBoost). It was revealed that Boruta produced the optimal number of features with n = 96, whereas RFC achieved the best prediction results compared to other classifiers, with around 95% accuracy. Consequently, the findings in this paper help to recognize the geospatial features that have impacts on the formation of COVID-19 and other infectious disease clusters at local scale. © 2022, Success Culture Press. All rights reserved.

Factors Affecting Student Satisfaction Towards Online Teaching: A Machine Learning Approach

During the outbreak of the Covid-19 pandemic, universities were forced to adopt technology and collaboration tools to reinforce online teaching and sustain their operations. This radical change pushes universities, researchers, educators, practitioners and decision makers to explore the perceptions of students and provide high quality online teaching operations. This study offers an understanding of the factors influencing students’ satisfaction with online teaching. Using data from an institutional survey, a machine learning approach is developed along with feature importance analysis using Permutation Importance and SHAP. The two techniques yielded similar results, where quality, interaction, and comprehension were the most significant predictors of satisfaction while student class, gender and nationality were insignificant. Such results support previous research conducted on similar data but with different statistical techniques. Other factors might be significant in the online environment such as student support, academic experience, and assessment. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models.

Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a discriminatory method for disease severity, defining the profile of patients with a higher risk of mortality. In this paper, we study the results of applying predictive models to data regarding COVID-19 outcome, using three datasets after age stratification of patients. The extreme gradient boosting (XGBoost) algorithm was employed as the predictive method, yielding excellent results. The area under the receiving operator characteristic curve (AUROC) value was 0.97 for the subgroup of patients up to 65 years of age. In addition, SHAP (Shapley additive explanations) was used to analyze the feature importance in the resulting models.

Decompensation Prediction for Hospitalized COVID-19 Patients

Since the start of the COVID-19 pandemic, hospitals have been overwhelmed with the high number of ill and critically ill patients. The surge in ICU demand led to ICU wards running at full capacity, with no signs of demand falling. As a result, resource management of ICU beds and ventilators has been a bottleneck in providing adequate healthcare to those in need. Short-term ICU demand forecasts have become a critical tool for hospital administrators. Therefore, using the existing COVID-19 patient data, we build models to predict if a patient's health will deteriorate below safe thresholds to deem admission into ICU in the next 24 to 96 hours. We identify the most important clinical features responsible for the prediction and narrow down the health indicators to focus on, thereby assisting the hospital staff in increasing responsiveness. These models can help the hospital staff better forecast ICU demand in near real-time and triage patients for ICU admissions as per the risk of deterioration. Using a retrospective study with a dataset of 1411 COVID-19 patients from an actual hospital in the USA, we run experiments and find XGBoost performs the best among the models tested when tuning parameters for sensitivity (recall). The most important feature for the four prediction tasks is the maximum respiratory rate, but subsequent features in order of importance vary between models predicting ICU transfer in the next 24 to 48 hours and those predicting ICU transfer in the next 72 to 96 hours. © 2022 IEEE.

COV-HM: Prediction of COVID-19 Patient's Hospitalization Period for Hospital Management Using SMOTE and Machine Learning Techniques

COVID-19 imposes burdens on hospitals. Evidence-based management and optimum resource allocation are essential. Understanding the time frame of support needs for COVID-19 patients staying in hospitals is vital for planning hospital resource allocation, especially in resource-constrained settings. Machine learning methods are being utilized in the approximation of the length of stay of a patient in the hospital. Four machine learning classifiers were used in this study to estimate the duration of hospitalization for patients in 11 different classes. Due to the dataset's imbalance, SMOTE was applied to eliminate the problem. The prediction accuracy of the K-Nearest Neighbors, Random Forest, Decision Tree, and Gradient Boosting classifiers was 73%, 69%, 58%, and 57%. The feature importance scores assist in the identification of vital features while building machine learning models. This research will assist responsible authorities in maintaining hospital services depending on the length of a patient's stay. © 2022 ACM.

Prediction Models for COVID-19 in Children

COVID-19 can also be acquired by children and compared to adults, most of the time they have only mild cases. Moreover, clinical manifestations in children are non-specific and can also be seen in other viral infections. Nonetheless, they are also susceptible to acquire the severe form of the disease requiring urgent hospitalization. In our study, we applied several machine learning models for COVID-19 prediction among children using only the clinical laboratory findings. The best performing models were obtained with random forest, decision tree, AdaBoost, and XGBoost with a 95–98% accuracy, 96–100% sensitivity and 92–96% specificity. Because of data imbalance, random oversampling was applied resulting to the improvement of performance metrics particularly of the best performing models. The top three important features for the best performing model (random forest) were leukocytes, platelets, and eosinophils. Our study has generated useful insights of using widely available, simple, and readily measurable laboratory blood counts in the development of robust models to predict COVID-19 in children with high reliability. These models can be used as decision-support tools in clinical practice when to request a COVID-19 diagnostic test, particularly in cases of limited RT-PCR tests. Tools aided with machine learning can thus be utilized to optimize constrained resources. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Risk assessment in COVID-19 patients: A multiclass classification approach.

Understanding SARS-CoV-2 infection that causes COVID-19 disease among the population was fundamental to determine the risk factors associated with severe cases or even death. Amidst the study of the pandemic, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied in many areas such as biomedicine. Using a dataset from the Mexican Ministry of Health, we performed a multiclass classification scheme for the detection of risks in COVID-19 patients and implemented three Machine Learning algorithms achieving the following accuracy measures: Random Forest (89.86%), GBM (89.37%) XGBoost (89.97%). The key findings are the identification of relevant components associated with different severities of COVID-19 disease. Among these factors, we found sex, age, days elapsed from the beginning of symptoms, symptoms such as dyspnea and polypnea; and other comorbidities such as diabetes and hypertension. This setting allows us to establish predicting algorithms to model the risk that an individual or a specific group of people face after contracting COVID-19 and the factors associated with developing complications or receiving appropriate treatment.

Feature Importance Analysis by Nowcasting Perspective to Predict COVID-19

The present work raises an investigation about prediction and the feature importance to estimate the COVID-19 infection, using Machine Learning approach. Our work analyzed the inclusion of climatic features, mobility, government actions and the number of cases per health sub-territory from an existing model. The Random Forest with Permutation Importance method was used to assess the importance and list the thirty most relevant that represent the probability of infection of the disease. Among all features, the most important were: i) the variables per region health stand out, ii) period comprised between the date of notification and symptom onset, iii) symptoms features as fever, cough and sore throat, iv) variables of the traffic flow and mobility, and also v) wheathers features. The model was validated and reached an accuracy average of 81.82%, whereas the sensitivity and specificity achieved 87.52% and the 78.67% respectively in the infection estimate. Therefore, the proposed investigation represents an alternative to guide authorities in understanding aspects related to the disease. [ FROM AUTHOR] Copyright of Mobile Networks & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Early Prediction of ICU Admission Within COVID-19 Patients Using Machine Learning Techniques

The COVID-19 pandemic has become a great challenge for healthcare systems due to the urgent need for ICUs that exceeded their capacity. Determining critical patients that require ICU transfer early will be valuable in optimising ICU resources and triage the patients. We propose a ML-based approach to predict ICU requirement within COVID-19 patients based on clinical data. A Mexican dataset of 7078737 cases and 38 attributes was considered in this paper. We trained four models MLP, DT, RF, and GB, on 70% of the data with five fold cross-validation and tested using the remaining 30%. Classification accuracies obtained were 97.72%, 97.14%, 99.06%, and 99.28%, respectively. Feature importance analysis based on GB model showed that age, hypertension, diabetes, obesity, pneumonia, days between symptoms onset and hospitalisation, location of the care unit, and private and public insurance are the main important factors. The latter factors highlight the importance of rapid and good quality care. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19.

Artificial intelligence (AI) technologies have been applied in various medical domains to predict patient outcomes with high accuracy. As AI becomes more widely adopted, the problem of model bias is increasingly apparent. In this study, we investigate the model bias that can occur when training a model using datasets for only one particular gender and aim to present new insights into the bias issue. For the investigation, we considered an AI model that predicts severity at an early stage based on the medical records of coronavirus disease (COVID-19) patients. For 5,601 confirmed COVID-19 patients, we used 37 medical records, namely, basic patient information, physical index, initial examination findings, clinical findings, comorbidity diseases, and general blood test results at an early stage. To investigate the gender-based AI model bias, we trained and evaluated two separate models-one that was trained using only the male group, and the other using only the female group. When the model trained by the male-group data was applied to the female testing data, the overall accuracy decreased-sensitivity from 0.93 to 0.86, specificity from 0.92 to 0.86, accuracy from 0.92 to 0.86, balanced accuracy from 0.93 to 0.86, and area under the curve (AUC) from 0.97 to 0.94. Similarly, when the model trained by the female-group data was applied to the male testing data, once again, the overall accuracy decreased-sensitivity from 0.97 to 0.90, specificity from 0.96 to 0.91, accuracy from 0.96 to 0.91, balanced accuracy from 0.96 to 0.90, and AUC from 0.97 to 0.95. Furthermore, when we evaluated each gender-dependent model with the test data from the same gender used for training, the resultant accuracy was also lower than that from the unbiased model.

COV-Doctor: A Machine Learning Based Scheme for Early Identification of COVID-19 in Patients

The COVID-19 pandemic brought about by the SARS-CoV-2 keeps on representing a critical danger to worldwide wellbeing. The most approved indicative test for Coronavirus, utilizing reverse transcriptase-polymerase chain response (RT-PCR) kit has deficiency sometimes in low-income countries. This adds to expanded disease rates and defers basic preventive measures. Successful screening empowers fast and effective analysis of Coronavirus and can relieve the burden on medical care services. Machine learning (ML) models are being used to anticipate the presence of COVID-19 in patients to support clinical staff worldwide, particularly with regards to restricted medical services assets. In this research, machine learning models have been developed to identify COVID-19 in the early stage of sickness using the information of symptoms and exterior activities of patients. Among the four machine learning classifiers, the Decision Tree and Extreme Gradient Boosting (XGBoost) performed equally better with 98% of accuracy, precision, and recall. The feature importance scores have been calculated also to understand the feature’s impact on the development of the machine learning model. The proposed work has outperformed the existing works with better execution. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree

Corporate financial distress prediction research has been ongoing for more than half a century, during which many models have emerged, among which ensemble learning algorithms are the most accurate. Most of the state-of-the-art methods of recent years are based on gradient boosted decision trees. However, most of them do not consider using feature importance for feature selection, and a few of them use the feature importance method with bias, which may not reflect the true importance of features. To solve this problem, a heuristic algorithm based on permutation importance (PIMP) is proposed to modify the biased feature importance measure in this paper. This method ranks and filters the features used by machine learning models, which not only improves accuracy but also makes the results more interpretable. Based on financial data from 4,167 listed companies in China between 2001 and 2019, the experiment shows that compared with using the random forest (RF) wrapper method alone, the bias in feature importance is indeed corrected by combining the PIMP method. After the redundant features are removed, the performance of most machine learning models is improved. The PIMP method is a promising addition to the existing financial distress prediction methods. Moreover, compared with traditional statistical learning models and other machine learning models, the proposed PIMP-XGBoost offers higher prediction accuracy and clearer interpretation, making it suitable for commercial use. • The model Combines a corrected feature selection measure and XGBoost. • Permutation importance can correct the bias of feature importance. • The model is validated on Chinese listed companies datasets over five metrics. • The model is proved to outperform several benchmark techniques. • The feature importance and partial dependence plot enhance model interpretation. [ FROM AUTHOR] Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance.

Coronavirus disease, Covid19, pandemic has a great effect on human heath worldwide since it was first detected in late 2019. A clear understanding of the structure of the available Covid19 datasets might give the healthcare provider a better understanding of identifying some of the cases at an early stage. In this article, we will be looking into a Covid19 Mexican Patients' Dataset (Covid109MPD), and we will apply number of machine learning algorithms on the dataset to select the best possible classification algorithm for the death and survived cases in Mexico, then we will study the performance of the enhancement of the specified classifiers in term of their features selection in order to be able to predict sever, and or death, cases from the available dataset. Results show that J48 classifier gives the best classification accuracy with 94.41% and RMSE = 0.2028 and ROC = 0.919, compared to other classifiers, and when using feature selection method, J48 classifier can predict a surviving Covid19MPD case within 94.88% accuracy, and by using only 10 out of the total 19 features.