Revolutionizing heart disease prediction with quantum-enhanced machine learning

Abstract

Abstract The recent developments in quantum technology have opened up new opportunities for machine learning algorithms to assist the healthcare industry in diagnosing complex health disorders, such as heart disease. In this work, we summarize the effectiveness of QuEML in heart disease prediction. To evaluate the performance of QuEML against traditional machine learning algorithms, the Kaggle heart disease dataset was used which contains 1190 samples out of which 53% of samples are labeled as positive samples and rest 47% samples are labeled as negative samples. The performance of QuEML was evaluated in terms of accuracy, precision, recall, specificity, F1 score, and training time against traditional machine learning algorithms. From the experimental results, it has been observed that proposed quantum approaches predicted around 50.03% of positive samples as positive and an average of 44.65% of negative samples are predicted as negative whereas traditional machine learning approaches could predict around 49.78% of positive samples as positive and 44.31% of negative samples as negative. Furthermore, the computational complexity of QuEML was measured which consumed average of 670 µs for its training whereas traditional machine learning algorithms could consume an average 862.5 µs for training. Hence, QuEL was found to be a promising approach in heart disease prediction with an accuracy rate of 0.6% higher and training time of 192.5 µs faster than that of traditional machine learning approaches.

Keywords

• Quantum computing
• Machine learning
• Ensemble methods
• Heart disease prediction