Search Results (411)

Background/Objectives: Missing data in clinical observational studies, such as out-of-hospital cardiac arrest (OHCA) registries, can compromise statistical validity. Single imputation methods are simple alternatives to complete-case analysis (CCA) but do not account for imputation uncertainty. Multiple imputation (MI) is the standard for handling missing-at-random (MAR) data, yet its implementation remains challenging. This study evaluated the performance of MI in association analysis compared with CCA and single imputation methods. Methods: Using a simulation framework with real-world Singapore OHCA registry data (N = 13,274 complete cases), we artificially introduced 20%, 30%, and 40% missingness under MAR. MI was implemented using predictive mean matching (PMM), random forest (RF), and classification and regression trees (CART) algorithms, with 5–20 imputations. Performance was assessed based on bias and precision in a logistic regression model evaluating the association between alert issuance and bystander CPR. Results: CART outperformed PMM, providing more accurate β coefficients and stable CIs across missingness levels. Although K-Nearest Neighbours (KNN) produced similar point estimates, it underestimated imputation uncertainty. PMM showed larger bias, wider and less stable CIs, and in some settings performed similarly to CCA. MI methods produced wider CIs than single imputation, appropriately capturing imputation uncertainty. Increasing the number of imputations had minimal impact on point estimates but modestly narrowed CIs. Conclusions: MI performance depends strongly on the chosen algorithm. CART and RF methods offered the most robust and consistent results for OHCA data, whereas PMM may not be optimal and should be selected with caution. MI using tree-based methods (CART/RF) remains the preferred strategy for generating reliable conclusions in OHCA research. Full article

The stability of ice structures in cold regions and polar environments has been increasingly challenged by global warming and climate change, making the accurate estimation of ice flexural properties essential. However, the flexural failure process of ice is highly complex, and the calculated flexural properties are influenced by multiple factors. Hence, several data-driven artificial intelligence models were developed to predict flexural strength, using classification and regression tree (CART), AdaBoost, and Random Forest methods, while the Elitist Ant System (EAS) was applied to optimize model parameters. The EAS procedure converged rapidly within ten iterations and effectively enhanced overall model performance. Compared with the single CART model, ensemble approaches exhibited higher prediction accuracy and better generalization, with AdaBoost achieving the best performance (R² = 0.736). Feature-importance analysis indicated that the testing method and specimen geometry had the greatest influence on the results, highlighting the importance of careful control of experimental conditions. The proposed ensemble–metaheuristic framework provides an efficient tool for predicting the mechanical behavior of ice and offers useful support for stability assessments of ice structures under changing climatic conditions. Full article

Background: The COVID-19 pandemic has put other infectious diseases, especially in children, into a new perspective. Our study focuses on two important viral infections: COVID-19 and influenza, which often present with similar clinical symptoms. Taking into consideration that the pathophysiology and systemic impact of the two viruses are distinct, which can lead to measurable differences in laboratory values, this study aimed to analyze laboratory features that differentiate between COVID-19 and influenza virus infections in pediatric patients. Methods: We statistically analyzed the routinely available laboratory data of 98 patients with influenza virus and 78 patients with COVID-19. Afterwards, the classification and regression tree (CART) method was performed to identify specific clinical scenarios, based on multilevel interactions of different features that could assist clinicians in evidence-based differentiation. Results: Significant differences between the two groups were observed in ALT, eosinophils, hemoglobin, and creatinine. Influenza-infected infants presented significantly higher leukocyte, neutrophil, and basophil counts compared to infants infected with COVID-19. Regarding children (over 12 months), significantly lower levels of ALT and eosinophil counts were observed in those with influenza compared to those with COVID-19. Furthermore, the CART decision tree model identified distinct profiles based on a combination of features such as age, leukocytes, lymphocytes, platelets, and neutrophils. Conclusions: After further refinement and application, such machine learning-based, evidence-driven models, considering the large scale of clinical and laboratory variables, might help to improve, support, and sustain healthcare practices. The differential decision tree may contribute to enhanced clinical risk assessment and decision making. Full article

Spatial monitoring of olive systems in arid regions is essential for understanding agricultural expansion, water pressure, and productive sustainability. This study aimed to map coverage and estimate olive plantation density (Olea europaea L.) in the Atacama Desert, Tacna (Peru) through the integration of UAV-satellite multispectral images and machine learning algorithms (CART, Random Forest, and Gradient Tree Boosting). Forty-eight optical, radar, and topographic covariates were analyzed. Fifteen were selected for coverage classification and 16 for plantation density, using Pearson’s correlation (|r| > 0.75). The classification maps reported an area of 23,059.87 ha (38.21%) of olive groves, followed by 5352.10 ha (8.87%) of oregano cultivation and 725.74 ha (1.20%) of orange cultivation, with respect to the total study area, with overall accuracy (OA) of 86.6% and a Kappa coefficient of 0.81. Meanwhile, the RF and GTB regression models showed R² ≈ 0.89 and RPD > 2.8, demonstrating excellent predictive performance for estimating tree density (between 1 and 8 trees per 100 m²). Furthermore, the highest concentration of olive trees was found in the central and southern zones of the study area, associated with favorable soil and microclimatic conditions. This work constitutes the first comprehensive approach for olive mapping in southern Peru using UAV–satellite fusion, demonstrating the capability of ensemble models to improve agricultural mapping accuracy and support water and productive management in arid ecosystems. Full article

The Northeast region of Thailand covers approximately 16.89 million hectares, with about 6.17 million hectares of seasonal rice cultivation and 2.85 million hectares affected by soil salinity—a major constraint to agricultural productivity in this region. This study develops an integrated data fusion framework combining multi-temporal Landsat-8 and Sentinel-2 imagery to train machine learning (ML) models for the prediction of rice yield and soil salinity, allowing for an analysis of their relationship. The field data comprised 380 rice yield and 625 soil electrical conductivity (EC) samples collected in 2023. Three ML models—Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Regression (SVR)—were applied for variable reduction and optimal predictor selection. RF achieved the highest accuracy for yield prediction (R² = 0.86, RMSE = 0.19 t ha⁻¹) and salinity estimation (R² = 0.93, RMSE = 0.87 dS/m) when using fused Landsat–Sentinel data. Spatial analysis of 5000 matched points showed a strong negative relationship between seedling stage EC and yield (R² = 0.71), with yields declining sharply above 5 dS/m and remaining below 1.5 t ha⁻¹ beyond 15 dS/m. These results demonstrate the potential of multi-sensor fusion and ensemble ML approaches for precise soil salinity monitoring and sustainable rice production. Full article

The path to becoming a veterinarian often begins well before university education, so understanding students’ career choices is essential. This study aimed to identify motivational characteristics of Hungarian high school students interested in veterinary medicine. Between December 2022 and March 2023, a questionnaire was distributed during high school career days, university open days, and via online platforms to collect data on students’ backgrounds, motivations, childhood animal exposure, and alternative career options. Recursive conditional Classification and Regression Tree (CART) models were used to identify motivational characteristics predicting veterinary career intentions. Among 428 respondents (74.1% female; mean age 17.8 years), a fondness for animals emerged as the predominant motivational factor; 97.4% had childhood pets, most commonly dogs. Human medicine was the main alternative career, followed by agriculture and veterinary nursing. Most students were interested in small animal medicine, while horse-related experience strongly predicted interest in equine practice. Interest in agriculture predicted preference for farm animal care. Students inclined toward non-clinical roles showed stronger interest in natural sciences and decided on a veterinary career later in life. These findings suggest that many students commit to veterinary medicine before age 12, highlighting the need for early engagement through competitions, camps, and extracurricular activities. Full article

Phytoplankton are fundamental to sustaining marine ecosystems and significantly influence the global carbon cycle. However, identifying their types accurately from satellite imagery remains a challenge. This study presents machine learning approaches for classifying phytoplankton types, including coccolithophores, diatoms, and dinoflagellates, using Second-generation Global Imager (SGLI) imagery aboard the GCOM-C satellite. Several algorithms were evaluated, with Random Forest (RF) and Gradient Tree Boosting (GTB) achieving the highest classification performance in classifying coccolitophores and diatoms. On the other hand, both RF and Classification and Regression Trees (CARTs) are effective for distinguishing dinoflagellates from surrounding water types. To assess model transferability, the developed machine learning models were applied in another sub-regions and on a different date of acquisition. The validation confirmed the ability of the model to generalize across sub-region and temporal variations in SGLI imagery. As a result, the potential of combined machine learning and SGLI imagery can improve phytoplankton detection, enabling large-scale monitoring at both regional and global levels. This paper highlights the importance of combining artificial intelligence with satellite-derived ocean color data to improve the monitoring of marine ecosystems. Full article

Collapsibility is a fundamental geotechnical property of loess that critically affects its engineering behavior. In this study, a comprehensive dataset comprising 9041 experimental records on the physical properties and collapsibility of loess from the Xi’an region was compiled. Six parameters were selected as model inputs: sampling depth (H), water content (w), plastic limit (w_P), plasticity index (I_P), compression coefficient (a_1–2), and compression modulus (E_s). Based on these inputs, prediction models for the loess collapsibility coefficient (δ_s) were developed using Gaussian Process Regression (GPR), Gradient Boosting Machine (GBM), Support Vector Regression (SVR), Radial Basis Function Neural Network (RBFNN), Classification and Regression Tree (CART), and Feature Tokenizer Transformer (FT-Transformer). Among these, GPR demonstrated the best predictive performance, achieving the lowest error (RMSE = 9.88 × 10⁻³) and the highest accuracy (R² = 0.844). Additionally, the coverage proportion of the 95% confidence interval of the GPR predictions reached 0.949. SHapley Additive exPlanations (SHAP) analysis for GPR further revealed that the compression coefficient exerted the greatest influence on δ_s (0.0149), followed by compression modulus (0.0080), water content (0.0068), plasticity index (0.0061), sampling depth (0.0061), and plastic limit (0.0052). The GPR-based prediction model offers significantly higher predictive accuracy than empirical models. The developed models provide a robust technical framework for the rapid estimation of loess collapsibility in the Xi’an region. Full article

Campaigns significantly impact overall company performance, making the measurement and prediction of campaign efficiency essential. This study proposes an integrated methodology that combines efficiency measurement with efficiency prediction for airline email campaigns. In the first part of the methodology, Data Envelopment Analysis (DEA) was applied to real airline campaign data to evaluate efficiency; this is the first study to analyze email campaign efficiency in this context. In the second part of the methodology, decision tree algorithms were employed to classify historical campaign data based on DEA scores, with the aim of predicting the efficiency of future campaigns—a novel approach in this context. A core dataset of 76 airline email campaigns with six inputs and two outputs was analyzed using output-oriented CCR (Charnes, Cooper, Rhodes) and BCC (Banker, Charnes, Cooper) models; 26 and 46 campaigns were identified as efficient, respectively. The analysis was further segmented by group size, seasonality, and route type. Efficient campaigns were then ranked via super-efficiency, and sensitivity analysis assessed variable and campaign effects. For prediction, decision tree algorithms (J48 (C4.5), C5.0, and CART (Classification and Regression Trees)) were employed to classify campaigns as efficient or inefficient, using DEA efficiency scores as the target variable and DEA inputs as attributes, with classification performed for both BCC and CCR core models. Class imbalance was addressed with SMOTE, and models were evaluated under stratified 10-fold cross-validation. After balancing, the BCC core model (BCC_C) yielded the most reliable predictions (overall accuracy 76.3%), with J48 providing the most balanced results, whereas the CCR core model (CCR_C) remained weak across algorithms. Full article

Seagrass meadows are vital for biodiversity and provide a plethora of ecosystem services, but significant losses due to human activity and climate change have been observed in recent decades. This study aims to evaluate whether the integration of Sentinel-2 composites, cloud computing (Google Earth Engine, GEE), and machine learning (ML) classifiers can produce accurate, scalable maps of seagrass habitats, enabling reliable estimates of associated carbon stocks. In this case study, we developed a methodological workflow for local-scale seagrass mapping in Cyprus, covering a total area of 310 km². ML techniques, specifically the Random Forest (RF) classifier and Classification And Regression Tree (CART), were employed in the main processing stage. The RF classifier achieved an overall accuracy of 73.5%, with a seagrass-specific F1-score of 69.4%. Class-specific F1-scores ranged from 63.2% for hard bottoms to 98.2% for deep water, accounting for variability in habitat separability. The workflow is designed to be scalable across Cyprus and potentially the broader EMMENA region (Eastern Mediterranean, Middle East, and North Africa). Based on the mapped extent of Posidonia oceanica meadows, preliminary estimates suggest a carbon stock of approximately 19,000 Mg C in Cyprus. Full article

This study was conducted to (i) determine the association between live body weight (BW) and biometric traits, (ii) examine the effect of biometric traits on BW of Tswana sheep using MARS and CART data mining algorithms, (iii) compare the performance of the algorithms and, finally, select the best algorithm for predicting BW in Tswana sheep. BW and sixteen biometric traits were measured from 392 Tswana sheep (males = 85 and females = 307) aged three to four years. Pearson’s correlation coefficients were used to establish the relationship between BW and biometric traits. The goodness of fit criteria were computed to assess the predictive performance of the data mining algorithms and select the best-fit model for predicting BW. The results showed that BW had a positive and significant correlation with heart girth (HG) (r = 0.99); thus, HG was used as a sole predictor of BW. The goodness of fit results indicated that MARS has a higher predictive performance than the CART algorithm, suggesting that the MARS algorithm can be used to predict BW Tswana sheep. These findings are an important statistical tool for the selection and concurrent improvement of useful biometric traits in genetic improvement programs to improve BW in Tswana sheep. Full article

Efficient public transportation systems are fundamental to achieving sustainable urban development. As the backbone of urban mobility, the coordinated development of rail transit and bus systems is crucial. The opening of a new rail transit line inevitably reshapes urban travel patterns, posing significant challenges to the existing bus network. Understanding passenger switch behavior is key to optimizing the competition and cooperation between these two modes. However, existing methods on the switch behavior of bus passengers along the newly opened rail transit line cannot balance the predictive accuracy and model interpretability. To bridge this gap, we propose a CART (classification and regression tree) decision tree-based switch behavior model that incorporates both predictive and interpretive abilities. This paper uses the massive passenger swiping-card data before and after the opening of the rail transit to construct the switch dataset of bus passengers. Subsequently, a data-driven predictive model of passenger switch behavior was established based on a CART decision tree. The experimental findings demonstrate the superiority of the proposed method, with the CART model achieving an overall prediction accuracy of 85%, outperforming traditional logit and other machine learning benchmarks. Moreover, the analysis of factor significance reveals that ‘Transfer times needed after switch’ is the dominant feature (importance: 0.52), and the extracted decision rules provide clear insights into the decision-making mechanisms of bus passengers. Full article

Background: The digital transition is reshaping healthcare systems through the adoption of telemedicine and electronic health records (EHRs). While these innovations enhance efficiency and access, their implementation unfolds within overstretched organizational settings characterized by workforce shortages, bureaucratic demands, and heightened psychosocial risks. Burnout, impostor syndrome, and the quality of organizational support have thus become pivotal constructs in understanding healthcare professionals’ digital preparedness. Methods: A cross-sectional online survey was conducted among 111 professionals employed at two San Donato Group facilities in Bologna, Italy. The battery included socio-demographic and occupational data, perceptions of digitalization, and validated instruments: the Maslach Burnout Inventory (MBI), the Clance Impostor Phenomenon Scale (CIPS), and the Work Organization Assessment Questionnaire (WOAQ). Descriptive analyses were complemented by Classification and Regression Trees (CART) to identify predictors of perceived digital preparedness. Results: Most respondents (88%) acknowledged the relevance of digitalization, yet 18% felt unprepared, especially women and administrative staff. Burnout levels were high, with 51% reporting emotional exhaustion, most notably among nurses and female participants. Impostor syndrome affected 43% of the sample, with nurses exhibiting the highest prevalence. CART analysis identified emotional exhaustion, impostor syndrome, and age as principal discriminators of digital preparedness. Conclusions: Our findings highlight the role of emotional exhaustion, impostor syndrome, and age in shaping perceived digital preparedness, underscoring the need for tailored training and supportive practices to ensure a sustainable digital transition. Full article

Prior work established the presence of six crosscutting patterns of clinically significant family maltreatment (FM) and psychological health (PH) problems among active-duty service members. Here, we develop a brief screener for these patterns via Classification and Regression Trees (CART) analyses using a sample of active-duty members of the United States Air Force. CART is a predictive algorithm used in machine learning. It balances prediction accuracy and model parsimony to identify an optimal set of predictors and identifies the thresholds on those predictors in relation to a discrete condition of interest (e.g., diagnosis of pathology). A 22-item screener predicted membership in five of the six classes (sensitivities and specificities > 0.96; positive and negative predictive values > 0.90). However, for service members at extremely high risk of clinically significant externalizing behavior, sensitivity and positive predictive values were much lower. The resulting 22-item brief screener can facilitate feasible, cost-effective detection of five of the six identified FM and PH problem patterns with a small number of items. The sixth pattern can be predicted far better than chance. Researchers and policymakers can use this tool to guide prevention efforts for FM and PH problems in service members. Full article

This study explores the concept of environmental responsibility and assesses the attitudes and perceptions of young adults towards environmental challenges. Applying a hybrid approach based on feature selection, machine learning methods (classification and regression trees (CART) and recursive feature elimination (RFE)) and statistical methods (chi-squared tests), we analyzed survey data from 500 students across three universities. The results reveal that 82% of students rate their climate knowledge as moderate or good, while 92% perceive climate change as a serious threat. Women are more likely than men to report engagement in pro-environmental initiatives. Students’ environmental orientation weakens in the middle years of study but re-emerges in the final year, possibly reflecting greater maturity and a stronger sense of responsibility before graduation. The willingness to establish sustainable enterprises does not always correspond to a high level of knowledge or daily environmental practices. While undergraduates report high levels of climate awareness, they often fail to translate this into concrete actions, indicating a gap between knowledge, motivation, and practice. The insights from the research can inform environmental education strategies, institutional practices, and youth engagement programs within higher education. Full article