Composite Artificial Intelligence

11A Composite Artificial Intelligence Based Framework for Heart Disease Prediction

M. Suresh^1*, M. S. Anbarasi², R. Rajmohan¹ and A. Anbarasi¹

¹Department of Computing Technology, School of Computing, SRM Institute of Science and Technology, Kattankalathur, Tamil Nadu, India

²Department of Information Technology, Pondicherry Technological University, Pillaichavady, Puducherry, India

Abstract

Cardiovascular Diseases (CVDs) remain one of the leading causes of death worldwide, and successful prevention and treatment rely heavily on early detection. Managing complex and multidimensional patient data poses a significant challenge for traditional diagnostic systems. Globally, the rising incidence of cardiovascular diseases presents major challenges to healthcare systems, requiring advanced predictive models for early detection and effective treatment. The complexity and unpredictability of patient information often hinder traditional methods, resulting in delayed actions. To enhance cardiovascular disease prediction, this research proposes a composite Artificial Intelligence (AI) model that integrates an Attention-based Convolutional Neural Network (ACNN) with multi-Bidirectional Long Short-Term Memory (m-BiLSTM) networks (ACNN-mBiLSTM). The proposed approach leverages mBiLSTM to capture temporal patterns from patient histories and CNN to extract critical spatial features from clinical data and medical images. To further improve predictive accuracy, an attention mechanism is employed ...