FAU Researchers Use Quantum Machine Learning to Predict Heart Disease

Insider Brief

• Florida Atlantic University researchers developed a quantum machine learning framework that achieved 90.26% accuracy in predicting heart disease using clinical data from 918 patients.
• The best-performing model, a Quantum Support Vector Machine using Angle Encoding, recorded 92.16% sensitivity, 83.42% specificity and an AUC of 0.93.
• The study evaluated five quantum feature mapping techniques and four quantum machine learning classifiers to assess their potential for healthcare analytics and clinical decision support.

Press release – Cardiovascular disease remains a major global health challenge, causing millions of deaths each year and imposing substantial healthcare costs. Early, accurate diagnosis is critical for improving outcomes and enabling timely intervention.

While conventional machine learning has shown promise in disease prediction, it often struggles with highly complex and nonlinear clinical data.

A research group from the College of Engineering and Computer Science at Florida Atlantic University, led by Arslan Munir, Ph.D., professor in FAU’s Department of Electrical Engineering and Computer Science and director of the Intelligent Systems, Computer Architecture, Analytics, and Security (ISCAAS) Laboratory, have developed a novel quantum machine learning framework that significantly improves heart disease prediction, achieving more than 90% accuracy.