A machine learning analysis of COVID-19 mental health data

Educational data mining (EDM) is the techniques used to discover the knowledge from student's data .it is used to improve the students' performance and teachers' performances as well. Since Machine learning (ML) models deals with numeric data, preprocessing of the categorical data is a must step to transform such data into accepted types by ML models. Categorical data is further divided into nominal and ordinal attributes in the dataset. The used data set was collected by using a learner activity tracker tool, which called experience API (xAPI). The purposed was to monitor the behaviors of students to evaluate the features that may impact the student performance (Amrieh, Hamtini & Aljarah, 2015). The dataset includes 480 student records and 16 features. The features are eitrher numeric or catgorical. In this paper, we study the effect of encoding some non-ordinal features as one-hot (dummy variables) on the students' performance prediction accuracy. We used techniques form ensemble methods such as Random Forest Trees, Boosting methods specifically namely gradient Boosted trees (GBT), and support vector machines. Also, we compared the performance of Random forest and Gradient boosted trees. We achieve a better result of 81% using random forest classifier. GBT has approximately same performance in all cases. SVM accuracy improved when used dummy variables. In addition, we show the importance of the behavioral features as they increase the model's accuracy. Random Forest outperformed other models considered in this study. At the end, the encoding methods as preprocessing steps can affect the performance accuracy of the models.

BackgroundFood addiction, characterized by the compulsive consumption of highly palatable foods, poses significant health risks, particularly among university students. This study investigates the prevalence of food addiction among Bangladeshi university students and its associations with mental health (depression, anxiety, stress, and insomnia) and behavioral factors (smoking, drug, alcohol use, and pornography consumption). Machine learning (ML) models were applied to enhance predictive accuracy.MethodsA cross‐sectional survey was conducted among 1697 participants across two Bangladeshi universities. Food addiction was assessed using the Modified Yale Food Addiction Scale 2.0 (mYFAS 2.0). Associations were examined using logistic regression and subgroup analyses by gender. Six ML models—K‐nearest neighbors (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), XGBoost, and CatBoost—were employed to improve classification performance.ResultsOverall, 13% of students met the criteria for food addiction, with higher prevalence among males (14.8%) than females (10.4%). In adjusted models, anxiety (AOR = 2.44, 95% CI: 1.43–4.16), stress (AOR = 1.74, 95% CI: 1.18–2.58), and pornography use (AOR = 1.74, 95% CI: 1.12–2.69) were significant predictors. Subgroup analyses showed that anxiety, stress, and pornography use were significant predictors only among males. Among ML models, KNN achieved the highest accuracy (85.3%), while RF demonstrated the best AUC‐ROC (0.697), confirming their utility in identifying at‐risk individuals.ConclusionsFood addiction affects a notable proportion of Bangladeshi university students and is strongly linked with anxiety, stress, and pornography use, particularly among males. Interventions should include cognitive‐behavioral therapy and stress management programs, digital hygiene education, and nutritional counseling tailored to student populations. ML‐based predictive models, such as RF and CatBoost, may be integrated into campus health systems to support early identification and personalized interventions.

Student’s mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be found, aiming for computational methods that learn information directly from data without relying on set parameters for a predetermined equation as an analytical method. This study aims to investigate the performance of Machine learning (ML) models used in higher education. ML models considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural) algorithm. Considering the factors of mental health illness among students, we follow three phases of data processing: segmentation, feature extraction, and classification. We evaluate these ML models against classification performance metrics such as accuracy, precision, recall, F1 score, and predicted run time. The empirical analysis includes two contributions: 1. It examines the performance of various ML models on a survey-based educational dataset, inferring a significant classification performance by a tree-based XGBoost algorithm; 2. It explores the feature importance [variables] from the datasets to infer the significant importance of social support, learning environment, and childhood adversities on a student’s mental health illness.

The evaluation of load-settlement behavior of piles is very crucial in meeting the serviceability criteria for pile analysis and design. The most reliable approach for estimating this behavior can be achieved by conducting pile load tests. However, due to the considerable expense and time requirement of such in-situ testing, the load-transfer methods have been used routinely in practice. In this paper, an alternative tree-based machine learning (ML) modeling is explored to predict the load-settlement behavior of axially loaded single piles from cone penetration test (CPT) data. Two variants of tree-based ML models, the random forest (RF) and gradient boosted tree (GBT), are developed in this study to estimate the load-settlement behavior of piles from CPT data (corrected cone tip resistance, qt, and sleeve friction, fs). A database of load-settlement curves of 64 static pile load tests and the corresponding CPT test data were compiled and used for the development of these ML models. The developed RF and GBT models are evaluated based on several statistical criteria. The load-settlement curves for six PLTs predicted using the developed RF and GBT models were compared with the measured data and the load-settlement curves predicted using the conventional load-transfer methods. The results demonstrated the great potential of tree-based ML (RF, GBT) models for predicting the load-settlement behavior of axially loaded piles from CPT data. The comparison clearly shows that the ML models outperformed the conventional load-transfer methods. Amongst the two ML models, the results show that the GBT model outperformed the RF model.

The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice.This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876).The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.

Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada.

This study evaluates the performance of four machine learning models: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes in analyzing visitor reviews of the Lokawisata Baturaden tourist attraction. Using 5-fold cross-validation, the study aims to determine which machine learning model best suits sentiment analysis on the Baturaden review data. This study was conducted through several stages, including data preprocessing, feature extraction, and the data training process. Case folding, text cleaning, tokenization, stopword removal, and stemming were performed during the data preprocessing stage. The feature extraction method used was TF-IDF. SMOTE was applied to increase data variation and address the data imbalance in the dataset. The results show that SVM provides the best performance with an accuracy of 0.937, an F1-score of 0.937, a precision of 0.943, and a recall of 0.937. Random Forest also performs well with an accuracy of 0.918 and an F1-score of 0.918, though slightly below SVM. KNN shows the lowest performance with an accuracy of 0.651 and an F1-score of 0.544, while Naive Bayes performs adequately with an accuracy of 0.845 and an F1-score of 0.841. Based on this evaluation, SVM is recommended as the best model for sentiment analysis of reviews, followed by Random Forest as a good alternative. The KNN model is not recommended due to its lower performance, while Naive Bayes can be considered for its speed and simplicity, although its results are not as good as SVM and Random Forest. These conclusions guide the selection of the optimal model to enhance understanding and visitor experience at the Baturaden tourist attraction.

Utilisation of machine learning algorithms to predict relapse risk following allogeneic stem cell transplantation (alloSCT) for patients with myeloid malignancies

BackgroundRheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study’s purpose was to construct machine learning (ML) models for the genomic prediction of RA and SLE.MethodsA total of 2,094 patients with RA and 2,190 patients with SLE were enrolled from the Taichung Veterans General Hospital cohort of the Taiwan Precision Medicine Initiative. Genome-wide single nucleotide polymorphism (SNP) data were obtained using Taiwan Biobank version 2 array. The ML methods used were logistic regression (LR), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and extreme gradient boosting (XGB). SHapley Additive exPlanation (SHAP) values were calculated to clarify the contribution of each SNPs. Human leukocyte antigen (HLA) imputation was performed using the HLA Genotype Imputation with Attribute Bagging package.ResultsCompared with LR (area under the curve [AUC] = 0.8247), the RF approach (AUC = 0.9844), SVM (AUC = 0.9828), GTB (AUC = 0.9932), and XGB (AUC = 0.9919) exhibited significantly better prediction performance. The top 20 genes by feature importance and SHAP values included HLA class II alleles. We found that imputed HLA-DQA1*05:01, DQB1*0201 and DRB1*0301 were associated with SLE; HLA-DQA1*03:03, DQB1*0401, DRB1*0405 were more frequently observed in patients with RA.ConclusionsWe established ML methods for genomic prediction of RA and SLE. Genetic variations at HLA-DQA1, HLA-DQB1, and HLA-DRB1 were crucial for differentiating RA from SLE. Future studies are required to verify our results and explore their mechanistic explanation.

In recent years, cardiovascular disease (CVD) has emerged as a leading cause of mortality worldwide, highlighting the urgent need for reliable predictive models to support early diagnosis and preventive care. This study conducts a comparative analysis of various machine learning models to assess CVD risk, focusing on their ability to accurately identify individuals at high risk based on clinical and demographic data. We evaluate multiple supervised learning algorithms, including logistic regression, random forests, support vector classifiers, K-nearest neighbors, gradient boosting, and AdaBoost, comparing their predictive performance in terms of accuracy, precision, recall, and F1-score. Additionally, we propose a novel hybrid model that combines Random Forest (RF) for feature selection and Deep Neural Networks (DNN) for classification, aiming to leverage the strengths of both approaches for more accurate CVD prediction. Our findings demonstrate that ensemble models such as random forests and gradient boosting achieve superior performance, with high accuracy (0.99) and balanced precision and recall values, outperforming simpler models like logistic regression and support vector classifiers. The hybrid model further enhances prediction accuracy, achieving 92.4% accuracy, 91.7% precision, 93.0% recall, and an AUC-ROC score of 96.0%. The analysis also underscores the importance of data preprocessing techniques, including normalization and handling of missing values, in optimizing model accuracy and stability. Notably, K-nearest neighbors also performed exceptionally well with a high F1-score across classes, highlighting its robustness for this task. This study provides a detailed examination of each model's strengths and limitations, including the proposed hybrid model, offering valuable insights for healthcare practitioners and data scientists in selecting effective machine learning models for CVD risk prediction. By integrating these models into healthcare systems, real-time risk prediction can be enhanced, ultimately supporting clinical decision-making and advancing personalized care in cardiovascular health. Keywords— cardiovascular disease, machine learning, risk assessment, predictive models, supervised learning, healthcare

Electronic commerce (e-commerce) brings huge advantages to businesses for selling products through multiple online shops. However, companies have difficulties in supervising the prices of products set by different retail shops on e-commerce platforms. Addressing these difficulties, we suggest a method to identify and predict products that sell at incorrect prices using a machine learning model combined price analysis. The study uses four machine learning models: K-nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multinomial Naive Bayes (MNB) and two text-based information extraction methods: BoW and TF-IDF to find to the best method. The research results show that the RF model and text-based information extraction method by the BoW provide more average accuracy than other specific models, when experimenting on the filter dataset the average accuracy after 10 runs are RF: 98.06%, SVM: 83.92%, MNB: 92.21%, KNN: 94.06%. Experimental results on the product dataset have an accuracy of RF: 83.02%, SVM: 55%, MNB: 79.33%, KNN: 79.36%.

Digital imaging

We analyzed commercial fleet operations data collected by a South Korean rent-a-car company, SK Networks Co. Ltd., to evaluate the differences between collision-free and collision-involved drivers with the ultimate goal of predicting driver collision risk. The first objective was to identify critical variables related to collision risk. The second objective was to build and compare classification models to predict the colli-sion involvement of a driver. Data used in the analysis were collected through Long-Range (LoRa) Internet of Things (IoT) modem-Fleet management system (FMS) devices, a first commercial implementation of LoRa modems in the vehicle. These devices have five main built-in modules, i.e., On-Board Diagnostics (OBD-II) Connector, GPS, LoRa modem, Gravity sensor, and Bluetooth. They can communicate with the vehi-cle, the driver’s smartphone, and the host server. Data from 3,854 drivers with a total of 2.19 million trips recorded in 2018 were explored. Out of these 3,854 drivers, 514 (13.3%) were involved in at least one collision. Predictor variables were selected based on previous research that uti-lized naturalistic data to identify factors affecting collision risk (Dingus et al., 2016; Tselentis, Yannis, & Vlahogianni, 2016; Bian, Yang, Zhao, & Liang, 2018; Jin et al., 2018). Forty-eight predictor variables that may affect collision risk were selected, which can be categorized into two groups: 27 variables were related to the business and the environment, characterized by how much drivers traveled, in what type of vehicles, on what types of roads, and during what times of the day; the other 22 variables were driving behavior-related variables, capturing overspeeding, potential fatigue, rapid speed changes, and counts of traffic regulation violations. After a feature selection phase based on univariate analysis, nine variables were select-ed to be used in the classification models. These selected vari-ables are running driving time (driving time excluding idling time), trip frequency per thousand kilometer driving, accumu-lated count of violation, accumulated amount of fine, the per-centage of trips driving a compact car (<1,000 cc), the per-centage of trips driving older than a 2016 car model, the per-centage of trips during 6 a.m.-9 a.m., the percentage of trips that ended during 2 a.m.-7 a.m., and the sum of rapid accelera-tion and deceleration frequencies per kilometer. A total of twenty classification models were built and compared to classify collision-involved and non-collision in-volved drivers: 5 classification modeling techniques (Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbor (kNN), Support Vector Machine (SVM) and Gradient boosted trees (GBT)) x 4 sampling methods (Up, Down, Smote, and No-sampling). The GBT-down sampled model showed the best classification performance according to Area under the Curve (0.804) and Area under the Precision and Recall Curve (0.406) statistics. Comparing relative variable importance val-ues for the best three classification models (GBT, RF, and LR), both running driving time and violation count were found to be the most influential variables, followed by the sum of rapid acceleration and deceleration frequencies, accumulated amount of fine, trip frequency per thousand kilometer driving, and the percentage of trips driving a compact car. These re-sults agree with the results of previous naturalistic studies: driver behavior-related variables are highly related to collision likelihood, although running driving time in our dataset was likely dictated by businesses. This dataset provided us with a unique opportunity to take an in-depth look at the relationship between collisions and business, environment, and driving behavior-related variables by using naturalistic data from newly-invented LoRa IoT-FMS devices. To the best of our knowledge, this study was the first naturalistic study connecting both driving data and various types of traffic violations (e.g., overspeeding, lane, sign, park-ing, toll fees, and fine amount). Interestingly, non-driving-related violation types such as parking or toll-fee violation counts were also strongly correlated with collision involve-ment; suggesting that collision-involvement is likely not just a skill issue but also an attitude issue regarding the law. In terms of industrial applications, this study suggests multiple oppor-tunities. Through a better understanding of the influential vari-ables related to collision-involvement (e.g., accumulated vio-lations), fleet operators can build policies to enhance their fleet safety, reducing collision rates and the associated costs. Further, in the long-term, this study can provide a framework for developing a Usage-Based Rent-a-car (UBR) service for car rental field, similar to Usage-Based Insurance (UBI), which can reduce drivers’ rental fees based on their driving behaviors.

Background/Objectives: Fetal health is essential in prenatal care, influencing both maternal and fetal outcomes. Cardiotocography (CTG) monitors uterine contractions and fetal heart rate, yet manual interpretation exhibits significant inter-examiner variability. Machine learning offers automated alternatives; however, class imbalance in CTG datasets where pathological cases constitute less than 10% leads to poor detection of minority classes. This study aims to provide the first systematic benchmark comparing five resampling strategies across seven classifier families for multi-class CTG classification, evaluated using imbalance-aware metrics rather than overall accuracy alone. Methods: Seven machine learning models were employed: Naïve Bayes (NB), Random Forest (RF), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), and Multi-Layer Perceptron (MLP). To address class imbalance, we evaluated the original unbalanced dataset (base) and five resampling methods: SMOTE, BSMOTE, ADASYN, NearMiss, and SCUT. Performance was evaluated on a held-out test set using Balanced Accuracy (BACC), Macro-F1, the Macro-Matthews Correlation Coefficient (Macro-MCC), and Macro-Averaged ROC-AUC. We also report per-class ROC curves. Results: Among all models, RF proved most reliable. Training on the original distribution (base) yielded the highest BACC (0.9118), whereas RF combined with BSMOTE provided the strongest class-balanced performance (Macro-MCC = 0.8533, Macro-F1 = 0.9073) with a near-perfect ROC-AUC (approximately 0.986–0.989). Overall, resampling effects proved model dependent. While some classifiers achieved optimal performance on the natural class distribution, oversampling techniques, particularly SMOTE and BSMOTE, demonstrated significant improvements in minority class discrimination and class-balanced metrics across multiple model families. Notably, certain models benefited substantially from resampling, exhibiting enhanced Macro-F1, BACC, and minority class recall without sacrificing overall accuracy. Conclusions: These findings establish robust, model-agnostic baselines for CTG-based fetal health screening. They highlight that strategic oversampling can translate improved minority class discrimination into clinically meaningful performance gains, supporting deployment in cost-sensitive and threshold-aware clinical settings.