Quantum-classical hybrid learning framework for Parkinson’s disease prediction using acoustic and clinical features
6 affiliations
- 1School of computer science, Zhuhai college of science and technology
- 2Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation
- 3Department of Computer Science and Engineering, Anurag Engineering College
- 4Department of Computer Science and Engineering, Faculty of Science and Engineering, Teesta University
- 5Department of Computer Science and Engineering, Lovely Professional University Phagwara
- 6Department of Computer Science, Turkish German University
Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, where early and accurate diagnosis plays a critical role in improving patient outcomes. While classical machine learning methods have shown strong performance in PD classification using clinical and acoustic data, they remain limited in capturing complex nonlinear feature interactions in high-dimensional spaces. This study explores the application of quantum machine learning (QML) techniques for PD prediction, focusing on Variational Quantum Classifiers (VQC) and Quantum Support Vector Machines (QSVM), and compares their performance with strong classical baselines including Logistic Regression, SVM, Random Forest, Grid Search–optimized models, and XGBoost. Experiments were conducted using stratified train–test splits and validated through cross-validation and statistical testing. Results show that classical models achieve competitive performance, with XGBoost reaching 91.9% accuracy. However, quantum models demonstrate slightly improved performance, with the VQC using EfficientSU2 achieving the highest accuracy of 93.1%, followed by the Real Amplitudes VQC (92.6%) and QSVM (92.2%). These improvements are consistent across evaluation metrics including F1-score and recall. Statistical analysis using 10-fold cross-validation indicates that the VQC (EfficientSU2) shows a significant improvement over classical baselines (p = 0.031), while other quantum models do not achieve statistical significance over XGBoost (p > 0.05). McNemar’s test on the independent test set confirms no significant difference in final prediction distributions between quantum and classical models. Overall, the findings suggest that quantum machine learning models are competitive with state-of-the-art classical approaches, offering marginal but consistent performance gains. This study highlights the potential of quantum-enhanced learning frameworks for biomedical classification tasks, while also emphasizing that their advantages remain incremental rather than definitive under current simulation-based implementations.
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BibTeX
@article{li2026quantumclassical,
title = {{Quantum-classical hybrid learning framework for Parkinson’s disease prediction using acoustic and clinical features}},
author = {Jiahua Li and N. Rama Rao and C. H. N. Santhosh Kumar and G. Madhukar Rao and Pranto Halder and Shakti Singh and Doğan Keskin},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62606-z},
url = {https://doi.org/10.1038/s41598-026-62606-z},
note = {CC BY},
}
RIS
TY - JOUR
TI - Quantum-classical hybrid learning framework for Parkinson’s disease prediction using acoustic and clinical features
AU - Jiahua Li
AU - N. Rama Rao
AU - C. H. N. Santhosh Kumar
AU - G. Madhukar Rao
AU - Pranto Halder
AU - Shakti Singh
AU - Doğan Keskin
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62606-z
UR - https://doi.org/10.1038/s41598-026-62606-z
AB - Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, where early and accurate diagnosis plays a critical role in improving patient outcomes. While classical machine learning methods have shown strong performance in PD classification using clinical and acoustic data, they remain limited in capturing complex nonlinear feature interactions in high-dimensional spaces. This study explores the application of quantum machine learning (QML) techniques for PD prediction, focusing on Variational Quantum Classifiers (VQC) and Quantum Support Vector Machines (QSVM), and compares their performance with strong classical baselines including Logistic Regression, SVM, Random Forest, Grid Search–optimized models, and XGBoost. Experiments were conducted using stratified train–test splits and validated through cross-validation and statistical testing. Results show that classical models achieve competitive performance, with XGBoost reaching 91.9% accuracy. However, quantum models demonstrate slightly improved performance, with the VQC using EfficientSU2 achieving the highest accuracy of 93.1%, followed by the Real Amplitudes VQC (92.6%) and QSVM (92.2%). These improvements are consistent across evaluation metrics including F1-score and recall. Statistical analysis using 10-fold cross-validation indicates that the VQC (EfficientSU2) shows a significant improvement over classical baselines (p = 0.031), while other quantum models do not achieve statistical significance over XGBoost (p > 0.05). McNemar’s test on the independent test set confirms no significant difference in final prediction distributions between quantum and classical models. Overall, the findings suggest that quantum machine learning models are competitive with state-of-the-art classical approaches, offering marginal but consistent performance gains. This study highlights the potential of quantum-enhanced learning frameworks for biomedical classification tasks, while also emphasizing that their advantages remain incremental rather than definitive under current simulation-based implementations.
SN - 2045-2322
ER -