Search Results (1,975)

The Chang 8 Member of the Jinghe Oilfield in the Ordos Basin is a low-porosity, ultra-low-permeability reservoir with many faults and fractures, complex structures, and strong heterogeneity. Conventional logging curves do not clearly distinguish among different structural units, making it difficult to identify the internal structures of fault-fracture reservoirs. Current methods mainly use logging curves and rock mechanical parameters. In these reservoirs, experiments are costly, numerical simulations take a long time, and identification is often inefficient. To improve identification accuracy and efficiency, this study developed a two-layer Stacking ensemble model for the Chang 8 Member. The dataset was derived from conventional well-log data from five wells in the Chang 8 Member and contained 816 labelled depth samples. Among them, 569 original samples from wells A1, A2, and A3 were used for model development, while 247 samples from wells JH55P10 and JH2301H were reserved for independent well-level validation. In the first layer, a support vector machine (SVM), XGBoost, and a random forest (RF) were used as the base learners. The hyperparameters of the base learners were optimized using grid search and K-fold cross-validation. In the second layer, multinomial logistic regression was used as the meta-learner to integrate the class-probability outputs of the base learners and generate the final predictions. Individual models showed limitations in distinguishing the three internal structural units of fault-fracture reservoirs. By integrating the complementary outputs of the base learners, the Stacking model achieved an overall accuracy of 0.89, exceeding the accuracies of the individual models on the internal hold-out test set. The results indicate that the proposed framework can improve the accuracy and class balance of multi-class identification on the present dataset and provide a practical approach for the detailed evaluation of internal structural units in low-porosity, low-permeability fault-fracture reservoirs. Full article

Fire occurrence in Southwest China is jointly shaped by meteorological conditions, topography, vegetation status, and human activities. To improve the interpretability and validation rigor of regional fire susceptibility assessment, this study developed a grid-day-based susceptibility assessment framework for Yunnan, Sichuan, Guizhou, and Chongqing using MODIS active-fire detections and multi-source environmental data from 2015 to 2024 at a 5 km grid resolution. A sensitivity analysis was conducted to determine the training sample configuration, and a 1:2 positive-to-negative sampling ratio was adopted. Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) were compared, and SHapley Additive exPlanations (SHAP), together with partial dependence plots (PDP), were used to interpret key drivers and their interactions. Data from 2015 to 2018 were used for model training, while data from 2019 to 2024 were used to evaluate the model’s cross-year transferability within the same study domain, rather than full spatiotemporal independence. The results show that the 1:2 sampling ratio achieved a favorable balance between fire detection and false-alarm control. In five-fold stratified cross-validation, RF outperformed LR and SVM (AUC = 0.9167; F1-score = 76.70%). In the cross-year transferability test, areas classified as high and very high susceptibility captured 62.04–68.95% of the observed fire points while accounting for less than 32% of the total area. Soil moisture and maximum temperature contributed most strongly to the model output, and their interaction revealed a pronounced dry-hot statistical response pattern associated with elevated susceptibility. Fire susceptibility also exhibited stable positive spatial autocorrelation, with hotspot areas concentrated in the dry-hot valleys near the Sichuan-Yunnan border and in central-southern Yunnan. Because the model was built with under-sampled negatives and same-day environmental matching, the output should be interpreted as a relative fire susceptibility index for spatial assessment and statistical attribution rather than as a calibrated occurrence probability or a forward-looking daily forecast. Full article

Diabetes mellitus is strongly associated with cardiovascular dysfunction and remains one of the leading contributors to morbidity and mortality worldwide. Early identification of diabetes-related cardiovascular alterations is essential for timely risk stratification and personalized clinical management. In the present study, an interpretable machine learning framework for diabetes classification in patients with cardiovascular disease was developed using routinely available clinical, biochemical, renal, and echocardiographic parameters. A retrospective dataset consisting of 131 cardiovascular patients was included in the final analysis, comprising 65 patients with diabetes mellitus and 66 patients without diabetes. Demographic, metabolic, renal, and cardiovascular variables, including age, body mass index (BMI), glycated hemoglobin (HbA1c), glucose concentration, estimated glomerular filtration rate (eGFR), troponin level, heart rate, and left ventricular ejection fraction (EF), were included in the analysis. Multiple supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, and Random Forest, were implemented and compared using repeated stratified cross-validation. Among the evaluated models, Random Forest demonstrated the highest classification performance, achieving a mean ROC AUC of 0.880 ± 0.050. Statistical analysis revealed significantly elevated HbA1c, glucose, and troponin levels together with reduced ejection fraction values in diabetic patients. Explainable artificial intelligence analysis using SHAP and partial dependence plots identified glucose concentration, HbA1c, age, and renal function as the dominant contributors to diabetes classification. Nonlinear relationships between metabolic and cardiovascular variables were additionally observed. The obtained findings demonstrate that interpretable machine learning approaches can provide effective discrimination between diabetic and non-diabetic cardiovascular patients while maintaining clinically meaningful interpretability. The proposed framework may contribute to future intelligent clinical decision-support systems and personalized cardiovascular risk assessment strategies. Full article

This paper proposes a Machine Learning (ML) framework designed to attribute liability between drivers and pedestrians in traffic crashes. This study applies classification algorithms and interpretability techniques to analyze judicial rulings related to pedestrian crashes in Badajoz, Spain, from 2015 to 2024. The primary objective is to identify recurring crash patterns and determine liability levels for the parties involved. Several classification algorithms were evaluated, including Support Vector Machines (SVM), Neural Network (NN), Decision Trees (DT), Boosted Trees (BT), Naïve Bayes (NB), Random Forest (RF), K-Nearest Neighbors (K-NN), and Logistic Regression (LR). Among them, the quadratic-kernel SVM achieved the highest overall performance. To address the severe class imbalance of the data, stratified k-fold cross-validation and the Synthetic Minority Oversampling Technique (SMOTE) were applied to enhance the robustness and generalization capability of the model. A multiclass classification framework was implemented, and SHAP (SHapley Additive exPlanations) was integrated to improve interpretability by quantifying the contribution of each feature to the model’s predictions. The analysis identified critical factors that play a significant role in determining liability outcomes: driver license status, crash location, lighting conditions, reaction time, and the presence of drugs or alcohol. This research aims to contribute to the legal domain. While most existing studies have focused on predicting injury severity, few have addressed liability attribution. This is a multifactorial task that requires a comprehensive analysis of judicial decisions. The results demonstrate that machine learning-driven liability attribution can support judicial decision-making and provide valuable insights for the development of proactive urban traffic safety strategies. Full article

Reference evapotranspiration (ET₀) represents the atmospheric demand for water from a well-watered vegetated surface and is a key component of the hydrological cycle and agricultural water management. This study evaluated the performance of seven machine learning (ML) models: linear regression (LR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Decision Trees (DT), and Cubist, for predicting monthly FAO-56 Penman–Monteith ET₀ in Benin. The target variable was calculated from data collected at six synoptic stations over the 2017–2021 period. Ten remote-sensing and topographic predictors were used: MODIS Land Surface Temperature (LST), six Sentinel-2 optical vegetation indices (NDVI, EVI, NDMI, NDWI, MSI, NDRE), elevation, and cyclic month encoding. Models were trained on the 2017–2019 period and evaluated on an independent temporal test set (2020–2021). All models showed positive predictive performance, with the BMA ensemble achieving the highest accuracy (RMSE = 7.0% of mean ET₀, R² = 0.802), followed by Cubist (RMSE = 7.3%, R² = 0.787) and DT (RMSE = 7.5%, R² = 0.776). The seven models were combined via Bayesian Model Averaging (BMA) with posterior weights estimated by the EM algorithm to produce 1 km monthly ET₀ maps for Benin for 2025. BMA-derived inter-model standard deviation provided spatially explicit uncertainty estimates, revealing that prediction uncertainty is greatest in the northern Sudanian zone during the dry season. The ET₀ target variable was constructed as a hybrid product combining station temperature observations with solar radiation, wind speed, and vapor pressure deficit extracted from the TerraClimate gridded reanalysis dataset; this methodological choice is discussed as a study limitation. Full article

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R² of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R² increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments. Full article

Monitoring the stability of multivariate quality processes is essential for ensuring product conformity and process reliability in industrial systems. Multivariate Exponentially Weighted Moving Average (MEWMA) control charts are widely used to detect small and persistent shifts in correlated quality characteristics. However, although MEWMA can identify out-of-control (OOC) conditions, it does not directly indicate which variables contribute to the detected signal. To address this limitation, this study reformulates multivariate control chart interpretation as an explainable supervised learning problem using data from an automotive production process. Several machine learning classifiers, including XGBoost, Random Forest, Support Vector Machines (SVM), LightGBM, Logistic Regression, CatBoost, and K-Nearest Neighbors (KNN), were trained using In-Control (IC)/OOC labels generated from MEWMA monitoring outcomes. Statistical tests were conducted to examine whether the observed performance differences among classifiers were statistically significant, while the computational efficiency of the framework was evaluated through a per-observation timing experiment. Among the evaluated models, XGBoost provided the most balanced overall classification performance and was further examined using the SHapley Additive exPlanations (SHAP) method. SHAP analysis enabled both global and local interpretations of model predictions by quantifying each variable’s contribution to OOC classifications. The findings indicate that combining MEWMA-based monitoring with explainable machine learning offers a practical and interpretable complement to analytical decomposition approaches in multivariate process monitoring. The proposed approach offers a practical, data-driven framework for explaining MEWMA-based IC/OOC classification decisions and identifying the relative contributions of variables in industrial quality-monitoring applications. Full article

Short-wave near-infrared (SW-NIR) spectroscopy provides a rapid and nondestructive sensing route for monitoring bread staling, but formulation-dependent moisture redistribution and starch retrogradation can make pooled spectral regression unstable. This study investigated a stratified SW-NIR modeling strategy for bread staling prediction using 324 spectra from control bread (CR) and two maltogenic $\alpha$-amylase treatments (EZ1 and EZ2). A global full-spectrum partial least squares (PLS) model was compared with bread-type-specific PLS models; competitive adaptive reweighted sampling (CARS), support vector machine recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble LASSO (MFE-LASSO) were then each coupled with PLS and evaluated within each bread type. The pooled benchmark achieved a root mean square error of prediction (RMSEP) of 2.28 days, whereas stratified full-spectrum PLS reduced this to 1.86, 2.14, and 2.15 days for CR, EZ1, and EZ2, respectively. In repeated wavelength-selection runs, MFE-LASSO was the most consistently competitive method across bread types. In the representative best-model comparison, MFE-LASSO-PLS yielded the strongest performance for CR (RMSEP = 1.71 days) and EZ1 (RMSEP = 1.43 days), while CARS-PLS gave the lowest RMSEP for EZ2 (2.00 days). An exploratory position-specific analysis within the CR subset further suggested that the middle crumb region carried stronger staling-related spectral information than the top and bottom regions. These results indicate that formulation-aware SW-NIR spectroscopic sensing is a practical strategy for nondestructive bread-staling assessment and that the optimal wavelength-selection method is bread-type-dependent. Full article

Spatial chromatic instability is currently one of the most robust methods for improving solutions proposed for image-based fire detection systems. Real flames exhibit erratic, turbulent local color variations, providing a more reliable discriminative signal than global color information alone, especially in visually ambiguous non-fire situations. This study proposes a generalizable feature representation based on the Spatial Chromatic Instability Index (ICCS) to measure local RGB variations (ICCS_R, ICCS_G, ICCS_B, and ICCS_T). Two public datasets comprising both fire image files and non-fire imagery were used. The Hilbert–Schmidt Independence Criterion (HSIC) and Silhouette coefficient analysis were used to quantify the statistical dependence between feature sets and the resulting cluster separation. To evaluate the practical discriminatory performance of spatial chromatic instability, three classifiers, i.e., Logistic Regression, Linear SVM, and Random Forest, were employed. To verify the proposed approach’s effectiveness, three deep learning models, Swin Transformer, MobileViT, and ViT-Base-16, were also employed for cross-checking. Performance metrics demonstrated that integrating ICCS features into global color features improved classification. Logistic Regression performed best overall on the Kaggle dataset when local ICCS features were included, achieving an accuracy of 0.935 and an F1-score of 0.958. For the Mendeley dataset, Linear SVM achieved an accuracy of 0.862 and an F1-score of 0.881. The ICCS is a robust, easy-to-understand, and fast approach for identifying fires. It has real potential in early warning systems, mainly due to its limited requirements for computing power. Full article

Mastitis is a major and costly dairy disease that reduces milk yield and quality and harms animal welfare. This study evaluated infrared thermography (IRT) combined with machine learning (ML) for non-invasive mastitis screening in dairy cows and explored links with biological and feeding-system variables in Egyptian farms. A total of 976 thermal udder images obtained from 488 Holstein cows were used, including 708 healthy and 268 mastitic images. Images were captured before milking, processed with CLAHE, resized to 224 × 224 pixels, and split using cow-level grouping before augmentation to prevent animal-level data leakage. The training set contained 780 original images and was augmented to a balanced 4708-image set (2354 per class), while the held-out test set remained unaugmented, with 196 original images (142 healthy and 54 mastitic). EfficientNetB3 with global average and max pooling extracted 3072 thermal features, and ten ML classifiers were evaluated. In the image-level hold-out evaluation, MLP achieved the best performance (accuracy = 86.22%, AUC = 0.9184, sensitivity = 74.07%, specificity = 90.85%), followed by SVM (accuracy = 83.67%, AUC = 0.8963). A separate group-based five-fold cross-validation yielded a more conservative AUC of 0.6812 ± 0.1323 and accuracy of 0.6244 ± 0.0642. Logistic regression analyses did not identify statistically significant associations between model predictions and somatic cell count (SCC), California Mastitis Test (CMT), blood biomarkers, or nutritional variables at p < 0.05. Ration A (Delta Misr) showed a higher observed mastitis incidence (20/40; 50.0%) than Ration B (Copenhagen; 16/45; 35.6%), but nutritional predictors were not statistically significant, indicating that farm-level confounding should be considered. Overall, IRT with ML remains a promising non-invasive screening approach, but broader multicenter datasets and independent external validation are needed before routine farm deployment. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is a common and serious sleep-related disorder that causes repeated interruptions in breathing during sleep. Traditional diagnostic methods, such as polysomnography, are accurate but costly, time-consuming, and unsuitable for large-scale screening. This study proposes and evaluates a lightweight diagnostic framework based on an Extreme Learning Machine (ELM) optimized by a set of basic and advanced metaheuristic optimizers. The model aims to evaluate whether metaheuristic optimization can improve ELM-based classification performance using structured demographic, clinical, and sleep-related predictors. Methods: Two real datasets were employed to train and evaluate the proposed framework: (i) a clinical OSA dataset with 274 subjects and 31 demographic/anthropometric and sleep-related predictors, and (ii) a public strongly imbalanced Sleep-Disordered Breathing (SDB) dataset with 500 subjects and 10 structured predictors. Metaheuristic algorithms are used to optimize ELM weights and biases, addressing the instability of random initialization and improving model generalization. The optimized models are evaluated against eight baseline classifiers, including logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), XGBoost (XGB), and a standard ELM classifier. Results: Results show that metaheuristic optimization moderately improves ELM on the OSA dataset, increasing ROC-AUC from 0.6527 to about 0.73 and accuracy from 0.6573 to about 0.69–0.70, while on the highly imbalanced SDB dataset, it yields modest ROC-AUC gains (from 0.5132 to about 0.544–0.548) with small decreases in accuracy and F1-score. We additionally assess class-imbalance handling on the SDB dataset and analyze feature importance with permutation importance and SHAP, which shows the models rely heavily on diagnosis-derived predictors. Conclusions: The proposed framework provides a lightweight ELM-based decision-support approach with low inference cost after offline optimization. The results suggest potential value for screening-oriented OSA/SDB classification, but further validation with larger cohorts and a screening-only feature set is needed before clinical implementation. Full article

Moisture content (MC) is an important indicator affecting the quality of dried lemon slices. To achieve rapid and non-destructive MC detection, this study developed a lemon slice MC detection model based on the fusion of image texture and spectral features. A total of 240 lemon slices were dried at 80 ${}^{\circ }$C, and hyperspectral imaging (HSI) data and reference MC values were collected at different drying times. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE) were used to select characteristic wavelengths. Image texture features were extracted using the gray-level co-occurrence matrix (GLCM), and the spectral features and image texture features were concatenated and fused. Kernel principal component analysis (KPCA) was then applied to reduce the dimensionality of the fused feature set. Finally, support vector machine (SVM), general regression neural network (GRNN), and partial least squares (PLS) models were established for MC detection. The results showed that the spectral-feature-based models achieved good predictive performance. The image texture-feature-based models also demonstrated predictive capability, whereas spectral–texture feature fusion further improved prediction accuracy. Among all models, the PLS model based on the spectral–texture fused features achieved the best performance, with a coefficient of determination of prediction ($R_p^2$) of 0.9890 and a root mean square error of prediction (RMSEP) of 0.1916 g/g in the prediction set. These results indicate that HSI combined with spectral–texture feature fusion provides a promising approach for rapid MC detection in lemon slices. Full article

Early detection of diabetes mellitus (DM) is essential for preventing disease progression and improving clinical outcomes. However, developing robust machine learning (ML) models for diabetes diagnosis is often constrained by limited data availability, privacy regulations, and challenges with data sharing. This study investigates a privacy-preserving synthetic data augmentation framework for classifying diabetic and non-diabetic blood serum samples using Fourier Transform Infrared (FTIR) spectroscopy. Two deep generative approaches, Autoencoders (AEs) and Generative Adversarial Networks (GANs), were evaluated for their ability to generate realistic synthetic FTIR spectra while preserving the statistical and biochemical characteristics of the original dataset. Synthetic datasets generated by the AE and GAN models were assessed using six ML classifiers: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Logistic Regression (LoR), and Decision Tree (DT). Model performance was evaluated using accuracy, precision, recall, F1-score, Receiver Operating Characteristic (ROC) curves, and Area Under the Curve (AUC). Results showed that AE-generated spectra retained stronger discriminative characteristics and were more easily distinguished from the original spectra, whereas GAN-generated spectra exhibited lower classifier separability, suggesting closer alignment with the original data distribution and greater realism for privacy-oriented data augmentation. Correlation analysis demonstrated high spectral fidelity for both approaches. Compared with the original spectra, AE-generated spectra achieved r = 0.9990 and R² = 0.9999, whereas GAN-generated spectra achieved r = 0.9982 and R² = 0.9965. The most prominent diabetes related spectral variations were observed in the carbohydrate (1000–1200 cm⁻¹), Amide I (~1650 cm⁻¹), and lipid-associated (3000–3500 cm⁻¹) regions. To explore the transferability of the proposed framework, a preliminary experimental feasibility study was conducted using independently acquired whole blood FTIR spectra. The generated spectra showed strong agreement with the measured whole blood spectra, demonstrating the potential applicability of the framework under alternative sampling conditions. Because the experimental cohort included only one diabetic volunteer, this analysis was intended solely as a proof-of-concept assessment of spectral feasibility and methodological transferability, rather than as a validation of diabetes classification performance. Overall, the findings demonstrate that synthetic data generation can effectively augment limited FTIR datasets while preserving privacy and key spectral characteristics. The proposed framework provides a promising foundation for privacy-aware biomedical data augmentation and future development of robust FTIR diabetes screening systems. The results should be interpreted as methodological evidence of feasibility and synthetic data utility rather than as evidence of clinical diagnostic readiness, as the serum dataset remains modest in size and the independent whole-blood experiment was intentionally exploring. Full article

Ovarian maturity in live female mud crabs (Scylla paramamosain) strongly affects harvest decisions and market value. Current ovarian maturity assessment relies mainly on expert-dependent methods that are subjective and destructive. Therefore, this study aimed to develop an interpretable, non-destructive image-based framework to classify crab ovarian maturity as immature or mature. A total of 240 crab image sets acquired using ventral external-view, dorsal external-view, and dorsal transillumination imaging were retained for analysis. Six primary morphometric features were semi-manually extracted from these views. External-view images quantified carapace width (CW), abdomen width (AW), abdomen area (AA), and sternum area (SA). Dorsal transillumination images yielded carapace area (CA) and ovary area (OA), an internal cue visualized through the intact carapace. To mitigate body-size variation, three ratio-based features—abdomen–carapace width ratio (ACWR), abdomen–sternum area ratio (ASAR), and ovary–carapace area ratio (OCAR)—were calculated. Between-class comparisons and correlation analyses were performed to guide candidate feature-set construction. Because OA and OCAR were strongly correlated, two reduced feature sets (Reduced 1 and Reduced 2) were designed to compare absolute ovary area with normalized ovary occupancy. Five feature sets—Raw, Ratio, Combined, Reduced 1, and Reduced 2—were evaluated using logistic regression (LR), support vector machine (SVM), and random forest (RF) classifiers. The Combined feature set, integrating all primary and ratio-based features, achieved the strongest mean cross-validated performance when paired with LR. On the held-out test set (n = 40), the final Combined-LR model achieved 0.950 accuracy and 0.997 ROC–AUC. On an independent practical implementation set (n = 40), the model correctly classified 39 specimens, achieving 0.975 accuracy. These findings may support non-destructive ovarian maturity screening and commercial grading in mud crab aquaculture. Full article

Background/Objectives: Chronic kidney disease (CKD) is an incurable and progressive condition; if diagnosed at an early stage, it would significantly reduce the risk of complications and enhance the outcomes for the patient. Methods: In this study, a custom dataset of 380 instances and 20 clinical attributes was used to develop and evaluate the machine learning (ML) models for reliable CKD prediction and to enhance the interpretability using explainable artificial intelligence (XAI) techniques. Artificial neural networks, C5.0, CHAID, logistic regression, linear support vector machines (L1 and L2 regularization), k-nearest neighbors (KNN), random tree, and deep neural networks were implemented. Correlation-based methods, recursive feature elimination, and LASSO were used for feature selection. SMOTE and SMOTETomek resampling techniques were used to address class imbalance. Three experimental set-ups were considered: (i) using SMOTETomek, (ii) with and without SMOTE, and (iii) grouped features according to the strength of correlation (high, moderate, low). Accuracy, precision, recall, F1 Score, AUC, and Gini index were used to evaluate the model’s performance. The pipeline was implemented in Python using the scikit-learn and imbalanced-learn packages. Results: Using SHAP and LIME, model interpretability was improved, with the KNN classifier obtaining the highest accuracy of 94.74% without SMOTE, and the C5.0 model obtained the highest accuracy of 92.98% with SMOTE. In the feature-group experiments, the L1-regularized linear SVM achieved high accuracy (89.47%) with highly correlated features. In general, both resampling methods improved model robustness, and feature selection methods reduced the model’s dimensionality with little loss in performance. Conclusions: The ML framework proposed is promising in predicting CKD with high accuracy and interpretability with relevance. By combining feature selection with class balancing and explainable AI, the model’s performance improves, and its clinical trustworthiness is enhanced. The results indicate the potential in using ML-based decision support systems for early-stage CKD diagnosis and personalized healthcare. Full article