• Aishwarya Prakash Verma
• Syed Rameez Ali
• Kshipra Pandey
• Priya Shrivastava
• Dr. Avinash Sharma

Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide, underscoring the urgent need for accurate and timely risk prediction models. This study explores the integration of Convolutional Neural Networks (CNNs) with ensemble learning techniques to improve cardiovascular risk prediction. CNNs, known for their efficacy in feature extraction from high-dimensional data such as medical images, are complemented by ensemble methods like bagging, boosting, and stacking to enhance predictive accuracy and robustness. By leveraging a multimodal dataset that includes imaging, clinical, and demographic data, the proposed hybrid model demonstrates superior performance compared to traditional machine learning approaches. Key metrics such as sensitivity, specificity, and area under the receiver operating characteristic (AUC-ROC) curve are significantly improved, making this approach a promising tool for personalized medicine. This research contributes to advancing precision cardiology by addressing challenges in data heterogeneity and predictive reliability.

Cardiovascular Risk Prediction, Convolutional Neural Networks, Ensemble Learning, Multimodal Data, Precision Medicine, Predictive Analytics, Machine Learning in Healthcare

"Leveraging Convolutional Neural Networks and Ensemble Learning for Enhanced Cardiovascular Risk Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.g637-g646, January-2025, Available :http://www.jetir.org/papers/JETIR2501669.pdf

"Leveraging Convolutional Neural Networks and Ensemble Learning for Enhanced Cardiovascular Risk Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppg637-g646, January-2025, Available at : http://www.jetir.org/papers/JETIR2501669.pdf