ArticleScientific reports2025
Predicting cardiovascular risk with hybrid ensemble learning and explainable AI.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Review
- Machine Learning for Coronary Heart Disease Prediction: Comparative Analysis of Framingham and Cleveland Subset of the UCI Dataset with SHAP-Based Interpretability.Epidemiologia (Basel, Switzerland) · 2026Article
- Interpretable Semi-federated Learning for Multimodal Cardiac Imaging and Risk Stratification: A Privacy-Preserving Framework.Journal of imaging informatics in medicine · 2026Article
- Predicting short birth intervals in Bangladesh using stacked machine learning and SHAP explainability: evidence from BDHS 2022.Reproductive health · 2026Article
- UbiQTree: Uncertainty quantification in XAI with tree ensembles.Patterns (New York, N.Y.) · 2026Article
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Decoding cardiovascular risk in Chinese middle-aged and elderly adults: a 9-year prospective study integrating machine learning with explainable AI based on CHARLS cohort.BMC medical informatics and decision making · 2026Article
- Sustainable and interpretable heart disease prediction: a clinical decision support approach for biomedical healthcare applications.Scientific reports · 2026Article
- Public health risk stratification using hybrid machine learning: a reproducible analysis of performance, stability, and risk attribution.Frontiers in bioinformatics · 2026Article
- Quantum-optimized spatio-temporal transformer for multimodal cardiovascular risk prediction from clinical and wearable data.Frontiers in cardiovascular medicine · 2026Article
- Genetic determinants of metabolic-inflammatory dysregulation and machine learning prediction of COVID-19.Frontiers in cellular and infection microbiology · 2026Article
- Redefining cardiometabolic biomarkers in the big data era: toward a personalized medicine-centered reconstruction of risk prediction models.Frontiers in cardiovascular medicine · 2026Article
- Multidimensional Visualization and AI-Driven Prediction Using Clinical and Biochemical Biomarkers in Premature Cardiovascular Aging.Biomedicines · 2025Article
- Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer.Frontiers in cardiovascular medicine · 2025Article
- Use of machine learning to predict hypertension based on BMI and routine lab data.Bioinformation · 2025Article
- Article
- Cognitive alignment in cardiovascular AI: designing predictive models that think with, not just for, clinicians.Frontiers in cardiovascular medicine · 2025Article
- Leveraging artificial intelligence for cardiovascular risk: a primary care perspective.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
- Article
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4 authors.
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Abstract
Cardiovascular diseases (CVDs) are still one of the leading causes of death globally, underscoring the importance of early and right risk prediction for effective preventive measures and therapeutic approaches. This study proposes an innovative hybrid ensemble learning framework that combines state-of-the-art machine learning models and explainable AI approaches to risk prediction for cardiovascular disease. Using a range of publicly accessible datasets, the suggested structure incorporates Gradient Boosting, CatBoost, and Neural Networks using a stacked ensemble architecture, resulting in more robust predictive performance than the constituent models. This is particularly interesting when visualised through techniques such as SHAP values, t-SNE and PCA projections which allows the study to explore the multidimensional aspects of the relationships between key risk factors including systolic/diastolic blood pressure, BMI, cholesterol-glucose ratio, alongside various lifestyle parameters. They build further on model interpretability through explainable AI methods so that clinicians can observe the involvement of each feature in generating the predictions. The hybrid model demonstrated strong predictive performance with an AUC-ROC score of 0.82, and confusion matrices showing a well-balanced classification of both positive and negative cases - achieving Precision: 81%, Recall: 83%, and F1-Score: 82% on the test dataset. The results highlight the potential of ensemble learning for addressing complex medical prediction problems and the need for models to be interpretable to ensure the trustworthiness of AI systems in healthcare settings. These findings provide an exciting opportunity toward better models of CVD risk prediction, potentially providing healthcare stakeholders with interpretable means to target treatments.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.