Search Results (2,015)

Background: This study aims to compare the performance of machine learning (ML) models developed to predict long-term mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (pPCI) and to investigate the prognostic value of novel inflammatory–metabolic indices. Methods: In this retrospective study, 329 consecutive STEMI patients who underwent pPCI (292 survivors, 37 deaths) were included. Five ML algorithms—Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN)—were developed for mortality prediction. Model performance was evaluated using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). SHAP (Shapley Additive exPlanations) analysis was used to interpret model decision mechanisms. Results: The mortality group had significantly higher door-to-balloon time (DTBT), Systemic Inflammatory Response Index (SIRI), pan-immune-inflammation value (PIV), whereas body mass index (BMI), Prognostic Nutritional Index (PNI), and Advanced Lung Cancer Inflammation Index (ALI) values were significantly lower (p < 0.001). Among the ML models, the XGBoost algorithm achieved the best performance, with 98.99% accuracy, a ROC-AUC of 0.999, and 100% sensitivity, correctly identifying all mortality cases. SHAP analysis identified DTBT, albumin level, and ALI score as the strongest predictors of mortality, in that order. Conclusions: The XGBoost algorithm provides high accuracy and reliability for predicting long-term mortality in STEMI patients. Beyond DTBT, integrating novel indices—especially ALI and TyG—into ML models may serve as a powerful clinical tool for early identification of high-risk patients and improved risk stratification. Full article

Background/Objectives: This study proposes a comprehensive classification framework for the automatic detection of Alzheimer’s disease using handwriting data. An enriched feature space is constructed by combining 18 baseline features extracted from raw handwriting signals with 30 additional features derived from established handwriting analysis studies, resulting in a total of 48 features. To enhance clinical practicality, a task reduction analysis is conducted by comparing the full dataset containing 25 handwriting tasks with a reduced dataset comprising 14 selected tasks. Methods: The proposed framework employs a two-stage evaluation strategy involving four feature selection methods (Random Forest Feature Importance, Extreme Gradient Boosting Feature Importance, L1 Regularization and Recursive Feature Elimination), three normalization techniques (Unnormalized, Min–Max and Z-Score), and five baseline machine learning classifiers (Random Forest, Logistic Regression, Multilayer Perceptron, XGBoost and Support Vector Machines). In the second stage, a dynamic ensemble learning strategy is introduced, where the most effective classifiers are adaptively selected for each cross-validation fold and integrated using soft and hard voting schemes. Results: The experimental results demonstrate that reducing the number of tasks leads to an improvement in average classification accuracy from 79.47% to 81.03%, while simultaneously decreasing training time and memory consumption by approximately 40% and 35%, respectively. The highest classification performance, achieving an accuracy of 94.20%, is obtained using the Hard Ensemble combined with L1-based feature selection. Conclusions: These findings highlight that the joint use of enriched feature representations, task reduction, and dynamic ensemble learning provides an effective and computationally efficient solution for handwriting-based Alzheimer’s disease detection. Full article

Background: Unplanned 30-day hospital readmissions, a key healthcare quality metric, are common and costly. Prediction models built on structured data often perform modestly, and the added value of unstructured clinical notes remains unclear. Methods: This retrospective cohort study included 4135 general medicine admissions to a tertiary Australian hospital between July 2022 and June 2023. Structured predictors included demographics, comorbidities, frailty, prior healthcare utilisation, length-of-stay, inflammatory markers, socioeconomic indicators, and lifestyle factors. We developed deep learning models using structured data alone, unstructured text alone, and a combined multimodal architecture integrating both modalities. For benchmarking, multiple classical machine learning models trained on structured features were evaluated using identical data splits, including logistic regression, XGBoost, random forest, gradient boosting, extra trees, and HistGradient Boosting. Model performance was assessed on a hold-out test set using ROC-AUC, accuracy, precision, recall, and F1-score. Results: Unplanned readmissions occurred in 24.3% of admissions. Among classical machine learning models, logistic regression achieved the highest discrimination (ROC-AUC 0.64), with no substantial improvement observed from ensemble methods. Structured-only deep learning achieved ROC-AUC 0.62. Unstructured text-only and multimodal models achieved ROC-AUCs of 0.52 and 0.58, respectively. Although overall discrimination of the multimodal model was lower than structured-only performance, it demonstrated improved sensitivity and F1-score for identifying patients who were readmitted. Prior hospitalisations, emergency department visits, and comorbidity burden were the strongest predictors. Conclusions: Structured EMR variables remain the main drivers of 30-day readmission risk. More complex classical machine learning models did not outperform logistic regression, and incorporating unstructured clinical text provided only modest improvement in identifying high-risk patients without enhancing overall discrimination. Full article

Soil salinization represents a critical constraint to sustainable agriculture in arid and semi-arid regions, where salinity threatens soil productivity, water quality, and ecosystem resilience. Soil salinity pattern prediction is complicated by tightly coupled landscape hydro-climatic processes, wherein the central Sabkha acts as a persistent salt sink, episodic inundation and intense evaporation concentrate dissolved salts, and a shallow saline groundwater table interacts with the semi-arid climate to drive surface salinization. Conventional mapping is laborious and lacks the precision needed to capture the spatio-temporal dynamics of soil salinity across landscapes. This study developed an integrated framework uniting multi-temporal Landsat imagery (2000–2025), hypsometric data, climatic indicators, and in situ soil electrical conductivity (ECe) measurements to model soil salinity dynamics using machine learning (ML), over the Sehb El Masjoune (SEM) semi-arid region, Morocco. A total of 233 soil samples were collected in the investigated area in 2022, 2023, 2024, and 2025 to assess the spatial variability to calibrate and validate modeling findings. To this end, three predictive algorithms, i.e., Gradient-Boosted Trees (GBT), Support Vector Regression (SVR), and Random Forest (RF) were assessed. Our findings showed that SVR achieved the highest predictive capability (R² = 0.76; RMSE = 32.91 dS/m), whereas SVR-based salinity maps revealed a distinct spatial organization of salinization processes, characterized by extremely saline soils (≥64 dS/m) concentrated in the central study area (i.e., SEM center) and a progressive decline toward adjacent agricultural lands (0–8 dS/m). Our results demonstrated that from 2000 to 2025, moderately to highly saline areas (≥16 dS/m) expanded by nearly 10%, driven by recurrent droughts and inefficient drainage. Hydroclimatic analysis confirmed that dry years (SPI: Standardized Precipitation Index ≤ −0.5) promoted net salinity build-up through the expansion and persistence of moderate-to-high salinity classes (≥16 dS/m), whereas wet years (SPI ≥ +0.5) favored temporary leaching and partial recovery, mainly within the low-to-moderate range. This integrative remote sensing–ML approach provides a robust and scalable framework for operational soil salinity monitoring, offering valuable insights for sustainable land-use planning in similar Sabkha’s data-scarce agroecosystems. Full article

Escalating global climate change has intensified the frequency and scale of wildfires in mountainous regions hosting transmission line infrastructure. These conflagrations act as extreme meteorological events, capable of generating localized heatwaves that compromise the air insulation of power lines and trigger protective relay operations, thereby posing systemic threats to regional grid stability. To enhance wildfire early-warning efficacy for grid security, this study formulates wildfire early warning for power transmission corridors as a regression-based risk prediction problem and proposes a hierarchical “global screening–local refinement” risk assessment framework. The primary contribution of this study lies in the integration of a machine-learning-based global wildfire risk screening model with tower-level spatial refinement using geographically weighted regression (GWR), enabling coordinated global–local wildfire risk characterization along power transmission corridors The framework employs a predictive model built on a Gradient Boosting Decision Tree algorithm, integrating geospatial and statistical analyses. A global risk model, utilizing historical data from the Himawari-8 satellite alongside meteorological, topographic, and anthropogenic variables, produces a composite risk index. This index is spatially interpolated via Kriging to generate stratified wildfire risk maps for broad-area assessment. For precise corridor-level analysis, these Globally Projected Risk Indices, along with localized terrain features, inter-tower clearance distances, and proximity to historical ignition points, are incorporated into a Geographically Weighted Regression model. This yields a spatially calibrated wildfire risk index along critical routes. The results show that the GBDT-based model achieved the best predictive performance among the evaluated regression models, with an R² of 0.626 and a mean squared error of 0.178. This approach offers a scientifically robust and operationally viable reference for wildfire prevention strategies in power line maintenance. Full article

Motivation: Accurate spectral reconstruction of printed halftone images is essential for achieving high-fidelity color reproduction and robust color management across modern printing systems. However, traditional physics-based models, such as the Yule–Nielsen and Clapper–Yule formulations, rely on simplified empirical assumptions and often fail to capture the complex nonlinear and asymmetric interactions induced by multi-ink overlays and substrate light scattering. Meanwhile, existing data-driven approaches based on single learning models exhibit limited capability in modeling the complementary and symmetrical characteristics inherent in halftone structures, resulting in suboptimal prediction accuracy and generalization performance. Method: To address these limitations, we propose a Stacking Ensemble Spectral Prediction (SESP) framework. The proposed method adopts a two-layer stacking architecture that integrates heterogeneous base regressors, including Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost 3.0.3), with Ridge Regression employed as the meta-learner for optimal prediction aggregation. This ensemble design enables effective modeling of both halftone pattern symmetry and complex substrate scattering behavior. Results: Extensive experiments conducted on printed halftone image datasets demonstrate the superior performance of the proposed SESP framework. Compared with the best-performing reference method (PCA-IPSO-DNN), SESP achieves relative reductions in RMSE and CIEDE2000 of 12.8% and 6.8% under illuminant A, 9.5% and 6.9% under D50, and 12.2% and 7.2% under D65, respectively. In addition, SESP consistently outperforms traditional physics-based models, including Yule–Nielsen and Clapper–Yule, in terms of both spectral prediction accuracy and colorimetric fidelity. These results confirm the effectiveness of the proposed framework in modeling the intricate nonlinear and asymmetric relationships between CMYK halftone patterns and spectral reflectance. Full article

Background: Bone Mineral Density (BMD) plays a crucial role in diagnosing osteoporosis, and early detection is essential to preventing complications such as osteoporotic fractures. However, access to dual-energy X-ray absorptiometry (DXA) screening remains limited in many healthcare settings. Objective: This study presents a fully automated artificial intelligence pipeline for BMD prediction from lumbar spine radiographs to enable opportunistic osteoporosis screening. Methods: The proposed system integrates automatic vertebral segmentation and a machine learning-based regression model for BMD prediction. A YOLO-based instance segmentation model was trained to automatically segment four lumbar vertebrae, achieving a high Intersection over Union (IoU) of 0.9. Radiomic features were extracted from the segmented vertebrae to capture advanced image characteristics and combined with clinical features from 2875 female patients. An eXtreme Gradient Boosting (XGBoost) regressor was trained to provide opportunistic BMD estimation. Results: The model achieved a mean absolute percentage error (MAPE) of 6% for BMD prediction. A classification model built from segmented vertebrae distinguished between osteoporosis, osteopenia, and normal bone with approximately 90% accuracy. Strong agreement between predicted and ground-truth BMD values was confirmed using Pearson correlation coefficient and Bland–Altman analysis. Conclusions: The proposed fully automated system demonstrates strong agreement with DXA measurements and potential for opportunistic osteoporosis screening in settings with limited DXA access. Further validation and refinement are needed to achieve clinical-grade precision for diagnostic applications. Full article

An assessment and prediction system for the quality of public water networks was developed, using Timișoara, Romania, as a case study. This was implemented on a Google Firebase cloud storage system and comprised twelve ML algorithms applied to test samples for drinkability and used in predictions of upcoming samples. The system compares 17 water quality parameters to the World Health Organization and public reports of Timișoara drinking water standards for 804 samples. The system provides real-time data storage, drinkability prediction for the reservoir water system, and rule-based critical water recommendations for elementary treatment in samples. The most accurate and best-calibrated against random forest, gradient boosting, and Logistic Regression algorithms was the decision tree algorithm of the ML models. The experimental findings also determine the regions of the worst and best water quality and propose respective treatment. In contrast to previous research and structures, the paper demonstrates an approved stable solution for smart water monitoring, correlating practical deployment with sophisticated data-based conclusions. The results contribute to enhancing public health, enhancing water management measures, and upscaling the system for larger-scale applications. Full article

Accurate estimation of Body Carcass Fat (BCF) is essential for evaluating the physiological condition of ewes. Traditional assessment via Body Condition Score (BCS) through palpation is inaccurate and subjective. BCF can now be predicted more precisely using objective measurements. This study presents a comparative analysis of eight machine learning (ML) models for predicting BCF in Coopworth ewes, using weight and RGB-image-based body measurements. Four non-linear regression methods and four neural network architectures were evaluated using a dataset of 74 ewes with 13 independent variables. The dataset was partitioned into training (52 ewes), validation (11 ewes), and testing (11 ewes) sets. The Gradient Boosting Regression achieved the highest predictive accuracy with an R² value of 0.9434 using body weight and width, followed by Ensemble Neural Network (R² = 0.9371) using body weight. The findings demonstrate the effectiveness of the Gradient Boosting Regression, Ensemble Neural Network and Random Forest tree-based approaches for morphometric prediction tasks in biological applications. BCF values obtained from image analysis were validated against those derived from computerized tomography (CT), considered the gold standard. These findings highlight the potential of image-guided, ML-driven models for objective, non-invasive, cost-effective assessment of ewe body composition in modern livestock systems. Full article

In acidic tea soil, boron (B) adsorption and desorption processes are dominated by the complex relationship between soil acidity, mineralogy, and organic matter. This study investigated B adsorption–desorption behavior in five acidic tea soils (pH 3.8–5.6) collected from the Eastern Black Sea region of Türkiye and evaluated the potential of machine learning (ML) algorithms to predict B desorption. Laboratory batch experiments were conducted using five initial B concentrations, and adsorption data were interpreted using the Langmuir isotherm model. Adsorption experiments indicated that B interacted with Fe/Al-oxide-containing clay minerals, which had low but favorable binding affinity, as indicated by Langmuir maximum adsorption capacities (Qmax) ranging from 46.5 to 181.8 mg kg⁻¹. Desorption experiments revealed a high degree of reversibility, particularly in soils with lower adsorption capacities, ensuring potential B leaching. To capture the governing B desorption, six machine learning (ML) algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Gaussian Process Regression (GP), Elastic Net Regression (EN), and Multivariate Adaptive Regression Splines (MARS)—were trained on 75 data points. Among the tested models, Elastic Net showed the highest predictive accuracy (R² = 0.735). This model does not replace adsorption experiments. It offers a within-assay determination of desorption given measured adsorption, which may reduce the requirement for separate desorption equilibration and analyses. Permutation importance analysis identified B_ads as the dominant predictor of B desorption, with smaller contributions from pH_ads and EC_ads. The results demonstrate that integrating laboratory experiments with machine learning provides an effective framework for predicting B mobility in acidic tea soils, offering a parameterized experimental framework for describing boron desorption behavior in acidic tea soils. Full article

The urinary sodium-to-potassium (Na/K) ratio is a clinically established predictor of blood pressure and cardiovascular risk. This study aimed to develop and rigorously evaluate machine learning models for estimating the urinary Na/K ratio using four easily obtainable physiological variables: body weight, systolic blood pressure, diastolic blood pressure, and pulse rate. A dataset of 82 participants was analyzed under a nested cross-validation framework to ensure strict generalization assessment. We first designed an attention-based deep learning model (MIDIP: Multi-Integrated Deep Ion Prediction). Although MIDIP showed reduced training error, nested validation revealed performance instability, indicating overfitting in this small-sample setting. We then compared classical machine learning models and ensemble strategies. Among all configurations, simple averaging of Random Forest, Gradient Boosting, and Linear Regression (Group A) achieved the best performance (MAE = 1.756, RMSE = 2.349, R² = 0.390). In contrast, incorporating a Transformer model (Group B) degraded performance (MAE = 1.855, R² = 0.294). Similarly, adaptive weighting (AWE) did not improve accuracy (Group A: MAE = 1.836, R² = 0.266; Group B: MAE = 2.133, R² = 0.035). These results demonstrate that, under limited sample conditions (N = 82), model simplicity and equal-weight ensemble integration provide superior generalization compared to attention-based or adaptively weighted deep architectures. The findings underscore the importance of strict validation and controlled model complexity when developing clinically applicable prediction models from small datasets. Full article

This investigation focuses on rice husk biochar (RHBC) as a sustainable filler in a polylactic acid (PLA) matrix. This study employs optimization techniques, including central composite design (CCD) and analysis of variance (ANOVA), to systematically evaluate the effects of key 3D printing parameters such as filler content (0 wt.%, 10 wt.%, 20 wt.%), nozzle temperature (190 °C, 200 °C, 210 °C), orientation angle (0°, 60°, 120°), and fill pattern (hexagon, triangle, and 3D infill). Furthermore, machine learning models are used to predict the mechanical properties of PLA/RHBC composites from experimental data. The effects of these parameters on tensile strength, Young’s modulus, and hardness were analyzed. The ANOVA results showed that filler content was the most influential factor for tensile strength and Young’s modulus, contributing 36.47% and 73.25%, respectively, compared to pure PLA. For hardness, both filler content and nozzle temperature were key contributors, with a 44.08% improvement over pure PLA. Machine learning models, including multiple linear regression (MLR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gradient Boosting, were used to predict the mechanical properties. Among these, Gradient Boosting achieved the best performance, with R² values of 97.79% for tensile strength, 98.79% for Young’s modulus, and 96.8% for hardness. This study provides a robust framework that combines experimental analysis, statistical design, and machine learning to optimize RHBC as an eco-friendly filler for the development of PLA composites for adoption in the automotive, sports and aerospace industries. Full article

This study examines the confined compressive strength (Fcc) of circular, square, and rectangular column geometries under varying confinement conditions. Results indicate that circular columns have the highest Fcc values, exceeding those of square and rectangular shapes. Increased confinement through clamps significantly enhances compressive strength. Five machine learning models, Linear Regression, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting, were used to predict Fcc based on geometric and confinement parameters. Linear Regression and Decision Tree models achieved moderate predictive performance, with R² values of 0.84 and 0.83, respectively, and relatively higher error measures (RMSE, MAE, and MAPE), indicating limited ability to capture complex nonlinear relationships in the data. In contrast, ensemble-based methods demonstrated superior performance. The Random Forest model improved the coefficient of determination to 0.90 while substantially reducing all error metrics, reflecting enhanced generalization through bagging. The boosting-based approaches yielded the best results, with AdaBoost achieving the highest R² value of 0.99 and the lowest RMSE, MAE, and MAPE among all models, followed closely by Gradient Boosting with an R² of 0.98. These results confirm that ensemble learning techniques, particularly boosting algorithms, yield more accurate and robust predictions than single learners for the problem studied. Data visualization techniques, including Regression Error Characteristic curves (REC) and SHapley Additive exPlanations (SHAP) value analysis, highlighted model performance and feature importance, emphasizing the roles of confinement and geometry in compressive strength. This research demonstrates the potential of machine learning to optimize structural engineering design and suggests further exploration of alternative shapes and confinement strategies to enhance structural integrity. Full article

Murals are vital cultural heritage assets, yet many are increasingly threatened by long-term natural weathering, environmental erosion, and human-induced damage. Given limited conservation resources, an objective method for prioritizing restoration is urgently needed. This study proposes an Individual-Aware Gradient Boosting Regression (IA-GBR) approach to predict the visual saliency of damaged mural regions by integrating physical damage characteristics, spatial location, and observer identity. We construct an eye-tracking dataset containing complete fixation records from multiple participants viewing diverse mural damage types. IA-GBR employs a two-level feature fusion strategy that combines damage, spatial, and individual features within a gradient boosting residual learning framework. The experimental results demonstrate that IA-GBR consistently outperforms baseline methods, including linear and ridge regression, SVR, decision trees, random forests, AdaBoost, and multilayer perceptrons. Feature importance analysis further reveals the relative contributions of individual differences, damage size, spatial position, and semantic factors to saliency formation. The proposed framework provides data-driven support for restoration prioritization and advances perception-aware saliency modeling in cultural heritage conservation. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article