Next-Generation Artificial Intelligence for Cardiovascular Disease Prediction: A Systematic Review of Emerging Paradigms
TL;DR
The findings indicate that tree-based ML models such as Random Forest, XGBoost, and Gradient Boosting shows the strong performance on structured clinical datasets, and ensemble learning models shows the superior performance and generalization capabilities across several distinct datasets.
Abstract
Cardiovascular disease is a main cause of mortality worldwide that need the development of reliable and data-driven prediction models for timely diagnosis and intervention. Conventional risk assessment methods depend on statistical scoring and handcrafted clinical features fail to capture complex nonlinear features in several cardiovascular existing data. This systematic literature review investigates recent advances in artificial intelligence-based CVD prediction that include machine learning, deep learning, hybrid, and ensemble learning models. The PRISMA-2020 strategy and Kitchenham guidelines, 110 studies published between 2013 and 2025 were systematically analyzed across diverse data modalities such as electronic health records, electrocardiograms, medical images, wearable sensors, and multimodal datasets. The findings indicate that tree-based ML models such as Random Forest, XGBoost, and Gradient Boosting shows the strong performance on structured clinical datasets. The DL-based models such as convolutional neural networks, long short-term memory networks, and transformer-based models effectively capture the high-dimensional physiological and imaging features. Hybrid model combined deep representations with clinical parameters to achieve predictive performance up to 98.00%, and ensemble learning models shows the superior performance and generalization capabilities across several distinct datasets. The challenges in cardiovascular disease detection such as limited multimodal datasets, very few external validation, insufficient explainability, and the absence of standardized benchmark evaluation parameters continue to show down the clinical translation. This review synthesizes current algorithmic developments, identifies critical research gaps, and highlights the potential of multimodal learning, explainable AI, federated learning, and clinically validated model for advancing next-generation cardiovascular risk prediction systems.