Cardiovascular disease (CVD) remains one of the main causes of death worldwide, and better risk prediction is needed to support early prevention and public health planning. Most existing prediction studies focus mainly on clinical variables, while environmental and regional influences are often underused. This study proposes a Hybrid Explainable Artificial Intelligence and Machine Learning (HXAI-ML) framework for area-level CVD risk prediction using health-related and satellite-derived environmental data at the Core-Based Statistical Area (CBSA) level. The dataset comprised 329 observations and 80 variables. Data preprocessing included median imputation, standardization, correlation-based feature filtering, and binary class labeling. To address class imbalance, several resampling strategies were evaluated, including Random Oversampling, SMOTE, Tomek Links, Instance Hardness Threshold, and hybrid combinations of these methods. Five ensemble classifiers were then assessed: Decision Tree, Random Forest, Extra Trees, Gradient Boosting, and Extreme Gradient Boosting. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, Kappa, Matthews Correlation Coefficient, MAE, MSE, and RMSE. Among all tested configurations, SMOTE combined with Extreme Gradient Boosting achieved the strongest performance, with 95.45% accuracy, 97.67% precision, 95.45% recall, and 96.55% F1-score. To enhance transparency, SHAP, LIME, and Permutation Importance Analysis were applied to identify and explain the most influential predictors. Overall, the proposed framework offers a robust and interpretable approach for population-level cardiovascular risk assessment and environmental health decision-making.
In recent years, Artificial Intelligence (AI) integration to Green Supply Chains (GSC) has been identified as a vital organizational practice for achieving profitability by reducing both environmental and social risks. So far, no study has explored the barriers to implementing AI integrate GSC. The current research will analyze the barriers to adopting the concept of AI in GSC through multi-stakeholder aspect with regard to opinions provided by industry practitioners, government, and academics. The study utilized a thorough literature analysis, and consultations with experts resulted in a list of 11 barriers to AI implementation in GSC. These are the low top management commitment, the high cost of investing in AI, low digital infrastructure etc. This study used Interpretive Structural Modeling (ISM) to represent the contextual relationships between the barriers, resulting in a hierarchical model. The results of the study show that obstacles are categorized into six hierarchic levels in the model with the top management commitment and Government support are identified as the leading drivers. Addressing the main cause of the problem, which is improving infrastructure, raising awareness, and developing professional ability, is critical to encouraging AI adoption. One limitation is that the study is based on the opinions of experts limited to Bangladesh, which may reduce generalizability. The implications include the recommendation that policymakers and supply chain managers focus on initiatives to address the driving barriers and develop supporting policies to assist AI-enabled practices in the sustainable process of resource-constrained situations.
Jannatul Ferdaus Disha, Kazi Md. Tanvir Anzum, M. Masum et al.· Engineering· 0 citations
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