Fusion of Hybrid AI and Dynamic Multi-Layered Feature Learning for Precision Driven Cardiovascular Disease Diagnosis

Cardiovascular diseases (CVDs) are responsible for most deaths worldwide, and new predictive and diagnostic models can aid in earlier detection and intervention. Traditional methods of diagnosis, such as electrocardiography and clinical interpretation, suffer from subject bias and variability. With advances in artificial intelligence (AI) and machine learning (ML), which enables the development of data-driven, computerized approaches for improving accuracy and efficiency, a new AI framework has been introduced, combining Regularized Discriminant Analysis (RDA), Multi-Layer Perceptron (MLP), and Light Gradient Boosting Machine (LGBM). Dynamic multi-layered feature learning allows the model to select strong predictors and attain superior accuracy, sensitivity, and specificity. This work introduces the clinical potential of hybrid AI models in CVD diagnosis while addressing big data analytics, model interpretability, and ethical challenges. Future research needs to take into account real-time patient monitoring, federated learning-based decentralized model training, and the optimization of AI deployment for resource-poor health care settings. The findings underscore the transformative power of AI driven hybrid models in early diagnosis, risk stratification, and improved patient outcomes, and how they can revolutionize cardiovascular disease diagnosis and treatment.