Conference articles · 2024
A Comparative Study of Classical and Quantum Algorithms for Heart Disease Prediction Using Patients' Vital Signs
2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) · Pages 1835–1840
Abstract
The aim of this study is to enhance the accuracy and reduce the time complexity of predicting cardiac illnesses by utilizing patient vital signs exclusively. The importance of timely treatment in critical situations where quick decisions are necessary is emphasized here. Precise predictions have the potential to prevent health deterioration and even save lives. By analyzing vast datasets (images, texts etc.) and detecting subtle patterns, quantum machine learning algorithms (QML) can offer more accurate results in a shorter period compared to classical algorithms. The feature set used here is a novel one to be used for the quick detection and early prediction of cardiovascular diseases, and also a convenient one for this task. To predict heart diseases or abnormalities, both classical and quantum machine learning techniques have been employed in this paper. We have developed four models based on Support Vector Machine (SVM), Neural Network (NN), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) and compared their performance on a balanced sample of a vast dataset for heart diseases prediction. After comparing the models’ performance, we found that Quantum Support Vector Machine (QSVM) performed best with an accuracy of 75% and 10-fold Cross-validation score of 80%.
Publication details
- Category
- Conference articles
- Authors & affiliations
- Venue
- 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)
- Publisher
- IEEE
- Record
- Pages 1835–1840
- Published
- 2024
- Cite this work
Paramita Basak Upama, Parama Sridevi, Masud Rabbani, Mohammad Syam, Abul Hasan Muhammad Bashar, Md. Rubaiyat Hossain Mondal, Rumi Ahmed Khan, Sheikh Iqbal Ahamed. “A Comparative Study of Classical and Quantum Algorithms for Heart Disease Prediction Using Patients' Vital Signs”. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), 1835–1840. (2024). https://doi.org/10.1109/COMPSAC61105.2024.00290.
Plain-language summary
Research takeaway
The authors compare classical and quantum support vector and neural network models for predicting heart disease from vital signs. Quantum support vector machines perform best in their evaluation, with 75% accuracy and an 80% ten-fold cross-validation score.
Research topics
- Heart Disease Prediction
- Vital Signs
- Quantum Machine Learning (QML)
- Classical Machine Learning (CML)
- heart disease prediction
- quantum machine learning
- classical machine learning
- vital signs
- cardiovascular risk
- comparative modeling
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