Related Experiment Video
Updated: Jun 29, 2025
05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
543
Revolutionizing heart disease prediction with quantum-enhanced machine learning
S Venkatesh Babu1, P Ramya2, Jeffin Gracewell3
1Department of CSE, Christian College of Engineering and Technology, Dindigul, India. venkateshflower6@gmail.com.
Scientific Reports
|March 29, 2024
Summary
Quantum machine learning (QuEML) shows promise in heart disease prediction, offering a 0.6% accuracy improvement and significantly faster training times compared to traditional methods.
Area of Science:
- Quantum computing
- Machine learning
- Healthcare technology
Background:
- Quantum technology advancements offer new avenues for machine learning in healthcare.
- Accurate diagnosis of complex disorders like heart disease remains a critical challenge.
Purpose of the Study:
- To evaluate the effectiveness of Quantum Enhanced Machine Learning (QuEML) for heart disease prediction.
- To compare QuEML's performance against traditional machine learning algorithms.
Main Methods:
- Utilized the Kaggle heart disease dataset (1190 samples).
- Assessed QuEML and traditional algorithms on accuracy, precision, recall, specificity, F1 score, and training time.
- Measured computational complexity in terms of training duration.
Main Results:
- QuEML demonstrated a 0.6% higher accuracy rate than traditional methods.
- QuEML achieved a training time 192.5 µs faster than traditional algorithms.
- Both approaches showed similar prediction rates for positive and negative samples.
Conclusions:
- QuEML is a promising approach for heart disease prediction.
- Quantum machine learning offers computational advantages in healthcare diagnostics.
- Further research into quantum algorithms for medical applications is warranted.
