FUSION OF MULTI-MODAL DEEP LEARNING AND EXPLAINABLE AI FOR CARDIOVASCULAR DISEASE RISK STRATIFICATION

Registration ID: IJNRD_304001

Published ID: IJNRD2312449

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Rishi Reddy Kothinti

Multi-Modal Deep Learning for Cardiovascular Risk Stratification, Explainable AI in Cardiovascular Disease Prediction, Artificial Intelligence In Precision Cardiology.

Early CVD risk assessment plays a critical role in global healthcare because cardiovascular disease continues as the dominant reason behind death and disability on a worldwide scale. Traditional risk assessment approaches use only structured clinical information because they cannot effectively benefit from unprocessed medical documents and diagnostic images and genetic databases. Multiple medical data sources such as electronic health records (EHRs) and medical imaging with genetic markers become more effective for prediction through multi-modal deep learning (MMDL) approaches. The combination of high complexity and limited interpretability within deep learning models prevents organizations from implementing them in practical medical decision support systems. The combination of Explainable AI (XAI) methods with multi-modal learning remains a fundamental approach to guarantee transparency and reliability and trustworthiness of CVD risk assessment. Recent innovations in deep learning methods for cardiac risk evaluation involve an assessment of transformer architectures and convolutional neural networks (CNNs) and graph neural networks (GNNs). This research analyzes SHapley Additive exPlanations (SHAP) as well as Local Interpretable Model-Agnostic Explanations (LIME) and attention-based visualization methods to improve model interpretability and establish trust among clinicians. The paper showcases a comparison of contemporary deep learning risk stratification systems that measure their performance in MACE prediction together with mortality assessment and disease evolution forecasting among various patient profiles. This paper explores the solutions to major AI challenges like model data heterogeneity as well as privacy problems and model generalization while presenting federated learning and ethical AI frameworks as solutions. Numerous deep learning models achieve superior prediction accuracy by uniting different types of medical data which far exceeds traditional single-modal approaches. Explainable frameworks must be integrated to make models clinically applicable despite their advantages in performance evaluation. Research must concentrate on creating dependable AI systems which maintain interpretability and respect ethical boundaries because these features promote adoption in cardiovascular health care. The combination of multi-modal deep learning with Explainable AI systems in this study shows the potential for artificial intelligence to revolutionize precise medical treatments of cardiovascular diseases including prevention and diagnostic assessment and therapeutic approaches.

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Rishi Reddy Kothinti (December-2023). FUSION OF MULTI-MODAL DEEP LEARNING AND EXPLAINABLE AI FOR CARDIOVASCULAR DISEASE RISK STRATIFICATION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(12), e519-e526. https://ijnrd.org/papers/IJNRD2312449.pdf

Volume 8 Issue 12, December-2023

Pages : e519-e526

Paper Reg. ID: IJNRD_304001

Published Paper Id: IJNRD2312449

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Author Type: Foreign Author

Country: Arlington, Texas, United States

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