Background: Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as a critical health issue. Extensive research has been undertaken to refine the forecasting of CVD in patients using various supervised, unsupervised, and deep learning approaches. Methods: This study presents HeartEnsembleNet, a novel hybrid ensemble learning model that integrates multiple machine learning (ML) classifiers for CVD risk assessment. The model is evaluated against six classical ML classifiers, including support vector machine (SVM), gradient boosting (GB), decision tree (DT), logistic regression (LR), k-nearest neighbor (KNN), and random forest (RF). Additionally, we compare HeartEnsembleNet with Hybrid Random Forest Linear Models (HRFLM) and ensemble techniques including stacking and voting. Results: Employing a dataset of 70,000 cardiac patients with 12 clinical attributes, our proposed model achieves a notable accuracy of 92.95% and a precision of 93.08%. Conclusions: These results highlight the effectiveness of hybrid ensemble learning in enhancing CVD risk prediction, offering a promising framework for clinical decision support.

Background In precision agriculture, especially in hydroponic systems where environmental management affects both production and resource efficiency, interpretable modeling of crop phenological stages is crucial. Typical visualization-based approaches are generally expensive and ecologically dependent. To classify and visualize lettuce growth stages (Seedling, Vegetative Growth, Heading, Bolting and Senescence) using temperature, humidity, total dissolved solids (TDS) and pH, this study proposed XGB-StageVision, a sensor-powered machine learning setup combining supervised and unsupervised learning techniques. Using XG Boost, LightGBM, CatBoost, ExtraTrees, Random Forest and LSTM under an 80/20 train-test split, a dataset of 3,169 unique observations from 70 hydroponically grown lettuce plants was evaluated. Methods and Results XGB-StageVision achieved 99.86% test accuracy, a ROC AUC of 1.00, an MCC of 0.9982 and perfect precision, recall, F1 scores and specificity across all classes, performing competitively with the best baseline models, including LightGBM, which achieved statistically comparable cross validation performance. The unsupervised t-SNE visualization showed clearly distinct structural clusters across growth stages, while SHAP revealed temperature as the strongest predictor (≈75% contribution), followed by TDS. Novel Contributions SHAP, LIME and t-SNE provided global, local and structural interpretability and transparent perspectives on environmental influences regarding plant development. Statistical analysis and 10-fold cross-validation were used to further test the model’s robustness, reliability and generalizability. Conclusion XGB-StageVision provides an IoT-ready, interpretable and imaging-free system for monitoring lettuce growth stages in real time. The framework combines supervised predictive modeling with unsupervised structural visualization to ensure transparency, sustainability and high performance, supporting resource-efficient, data-driven strategic planning in contemporary hydroponic agriculture to lower monitoring costs, optimize inputs and improve yield consistency.

Background:Liver diseases constitute one of the most prevalent global health problems that cause millions of deaths every year. Identification of potential patients even prior to their diagnosis could improve the efficacy of screening procedures and ensure timely interventions. Despite promising results yielded by Al and ML techniques in disease diagnostics, current models tend to be opaque and focus on binary disease prediction rather than pre-symptom detection. Methods: We propose a novel XAI-LiverSHAPNet framework to detect for liver disease stratification by employing ensemble machine learning. A publicly accessible synthetic dataset containing 1,700 patients records with 11 features was first prepared by removing outliers and imputing missing values and SMOTE oversampling was performed in order to balance classes in the data. LR, SVM, DT, RF, GB, KNN, CatBoost, XGBoost, and Extra Trees were tested individually and as stacking and voting ensembles. Latent relationships within the data set were discovered using t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). Unlike previous studies, the proposed model allows the identification of patients at high risk on the basis of the existing characteristics, facilitating risk stratification without necessarily needing data on symptoms. Additionally, conducted study further contributes methodological novelty through feature-level ablation analysis to evaluate the impact of key variables. The XAI-LiverSHAPNet framework follows a three-tiered analysis pipeline featuring data preprocessing and balancing, ensemble modeling, and interpretation through explainable AI. Results: XAI-LiverSHAPNet outperformed baseline and ensemble competitors, achieving a specificity of 97.19%, ROC-AUC of 96.61%, precision of 94.62%, recall of 94.24%, F1-score of 94.38%, and mean cross-validation accuracy of 93.12% (SD = 0.0151). The SHAP approach found that AlcoholConsumption, LiverFunctionTest, and Age had the greatest impact on the outcome, with the removal of all three parameters significantly decreasing model accuracy to around 70 percent. The statistical analysis revealed that the suggested model provided significantly better results than the Voting method for probabilistic calibration measures, namely Brier Score and Log Loss (p < 0.05). Conclusion: The presented XAI-LiverSHAPNet framework shows promise as a clinical decision support tool for liver disease risk stratification, where further validation is necessary on large-scale, multi-center data.

Stroke is a leading cause of mortality worldwide, making early and accurate risk prediction aimportant for clinical decision support. This study proposes a trustworthy, causality-aware machine learning framework for stroke risk prediction and digital twin-based counterfactual analysis using the publicly available Kaggle dataset healthcare-dataset-stroke-data.csv, comprising 5,110 patient records with 11 clinical and demographic features. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied exclusively to the training data. Six machine learning models Random Forest, XGBoost, CatBoost, ExtraTrees, LightGBM and HistGradientBoosting are evaluated using class-wise precision, recall, F1-score, test accuracy and 10-fold cross-validation. XGBoost achieves the highest reported test accuracy of 98.65%, with a stroke-class recall of 100.00% and F1-score of 87.83%, while CatBoost achieves the highest mean cross-validation accuracy of 94.83% ± 0.34%. SHapley Additive exPlanations (SHAP) identify smoking status, gender, work type and residence type as influential predictors, alongside glucose level, age and body mass index. Structural causal models, directed acyclic graphs, the PC algorithm and average treatment effect analysis investigate potential causal relationships among stroke risk factors. Digital twin-based counterfactual simulations examine hypothetical hypertension and glucose-level interventions. In one high-risk case, predicted stroke probability decreases from 99.93% to 65.19% under the reduced-glucose scenario. CatBoost achieves the fastest prediction time of 0.0027 seconds, while XGBoost requires 0.4913 seconds for training and 0.0158 seconds for prediction. These findings demonstrate the potential of integrating machine learning, explainability, causal analysis and counterfactual simulation for individualized stroke risk assessment. Further external validation and clinical evaluation are required before real-world deployment.