Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education

With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), K-Nearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as accuracy, specificity, sensitivity, F1 count and precision, have been used to evaluate the performance of each model. The results have shown that all five models can provide optimal results in terms of prediction. For example, the RF and XGB models presented the best performance with an accuracy rate of 92%, outperforming the other models. In consequence, it is suggested to use these two models RF and XGB for prediction of students' adaptability level in online education due to their higher prediction efficiency. Also, KNN, SVM and LR models, achieved a performance of 85%, 76%, 67%, respectively. In conclusion, the results show that the RF and XGB models have a clear advantage in achieving higher prediction accuracy. These results are in line with other similar works that used ML techniques to predict adaptability levels. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Machine Learning Applications in Forecasting of COVID-19 Based on Patients' Individual Symptoms

Predicting the COVID-19 outbreak has been studied by many researchers in recent years. Many machine learning models have been used for the prediction of the transmission in a country or region, but few studies aim to predict whether an individual has been infected by COVID-19. However, due to the gravity of this global pandemic, prediction at an individual level is critical. The objective of this paper is to predict if an individual has COVID-19 based on the symptoms and features. The prediction results can help the government better allocate the medical resources during this pandemic. Data of this study was taken on June 18th from the Israeli Ministry of Health on COVID-19. The purpose of this study is to compare and analyze different models, which are Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayesian (NB), Decision Tree (DT), Random Forest (RF) and Neural Network (NN). © 2021 ACM.