Computational Intelligence for Heart Disease Prediction and Detection: A Comparative Study of Machine Learning, Deep Learning, and Quantum Algorithms

Abstract

: Heart disease continues to be one of the most serious global health challenges, affecting people’s quality of life and placing a heavy burden on health-care systems. To improve early diagnosis and support better patient outcomes, this study examines a computational approach that brings together methods from machine learning, deep learning, and emerging quantum computing. Traditional machine learning models offer interpretable insights when working with structured clinical data, while deep learning techniques are well suited for identifying subtle patterns in complex sources such as ECG signals and cardiac imaging. In addition, quantum computing methods such as quantum support vector machines and variation quantum circuits show promise in exploring features more efficiently and speeding up optimization, potentially overcoming some of the limitations of classical computation. Early comparative results suggest that, when adequate quantum resources are available, hybrid quantum classical models can shorten training time and improve prediction accuracy. Overall, this integrated approach highlights how advanced computational techniques can support more effective health-care delivery, strengthen preventive care, and enable real-time, scalable detection of cardiac diseases.