A Hybrid Intelligent Framework for Cardiovascular Disease Diagnosis Using Multi-Layered Ant Colony Optimization and Enhanced Deep Learning

Abstract

This study proposes a novel diagnostic framework by combining Multi-layered Ant Colony Optimization with advanced deep learning for cardiovascular disease diagnosis. This system includes three major components: the MACO Module for dynamic feature selection, the Enhanced Deep Learning Neural Network with attention-based architecture, and the Advanced Bayesian Optimization System for automated parameter tuning. With intelligent preprocessing and adaptive feature extraction, this framework is capable of analyzing intricate medical datasets. It saves a lot of manual configurations with increased processing efficiency and thus is especially valuable for clinical applications where expert knowledge about system optimization may not be available. Performance evaluation shows strong diagnostic capability in various patient cases, which establishes the potential of this framework as a robust tool for the diagnosis of cardiovascular diseases in real-world healthcare settings.