AI-driven multimodal analysis for heart disease diagnosis: a computational approach to reliable and efficient healthcare systems

Recent advancements in machine learning (ML) and artificial intelligence (AI) are transforming the landscape of healthcare, particularly in the diagnosis of complex diseases like heart disease. This paper introduces a novel AI-driven methodology for heart disease diagnosis, utilising a combination of biological data and ECG signals through a multi-algorithm approach. By addressing critical challenges in data integrity, model accuracy, and computational efficiency, this research develops a robust, scalable system that ensures both high performance and low resource consumption which is suitable for real-time healthcare applications. The proposed system leverages advanced AI models, including a tailored Convolutional Neural Networks (CNNs) architecture, to perform in-depth analysis while ensuring computational efficiency. By integrating AI systems with healthcare practices, this work demonstrates how next-generation computational models can improve diagnostic decision-making, patient outcomes, and the overall efficiency of healthcare delivery.