COMPARATIVE ANALYSIS OF CLASSICAL AND QUANTUM SVM MODELS ON MEDICAL DIAGNOSIS DATASETS

Abstract

Quantum-assisted machine learning approaches have become a significant area of research in the healthcare domain by offering alternative solutions to classical methods, particularly when dealing with high-dimensional and complex datasets. This study presents a comparative evaluation of the classification performance of classical Support Vector Machines (SVM) and quantum-based algorithms Quantum Support Vector Machine (QSVM) and Pegasos-QSVM on healthcare data. Experimental analyses were conducted using three distinct medical datasets related to liver disease, breast cancer, and heart failure. The results demonstrate that the QSVM model consistently achieved the highest and most stable classification accuracy. Although the Pegasos-QSVM model achieved comparable accuracy rates in certain configurations, its performance was generally more variable. Nevertheless, thanks to its lower computational cost and faster processing time, Pegasos-QSVM emerges as a promising alternative, particularly in resource-constrained environments. The findings suggest that quantum-assisted models can deliver performance levels competitive with classical approaches, particularly highlighting the effectiveness of QSVM on small- to medium-sized datasets.

Keywords

• Pegasos-QSVM
• QSVM
• Medical Datasets
• Quantum Machine Learning

TIBBİ TANI VERİ SETLERİ ÜZERİNDE KLASİK VE KUANTUM SVM MODELLERİNİN KARŞILAŞTIRMALI ANALİZİ

Abstract

Kuantum destekli makine öğrenimi yaklaşımları, özellikle yüksek boyutlu ve karmaşık veri kümeleriyle çalışırken klasik yöntemlere alternatif çözümler sunarak sağlık alanında önemli bir araştırma konusu hâline gelmiştir. Bu çalışma, sağlık verileri üzerinde klasik Destek Vektör Makineleri (SVM) ile kuantum tabanlı algoritmalar olan Kuantum Destek Vektör Makinesi (QSVM) ve Pegasos-QSVM'nin sınıflandırma performanslarının karşılaştırmalı bir değerlendirmesini sunmaktadır. Karaciğer hastalığı, meme kanseri ve kalp yetmezliğiyle ilgili üç farklı tıbbi veri seti kullanılarak deneysel analizler gerçekleştirilmiştir. Elde edilen sonuçlar, QSVM modelinin tutarlı bir şekilde en yüksek ve en istikrarlı sınıflandırma doğruluğunu sağladığını göstermektedir. Pegasos-QSVM modeli belirli konfigürasyonlarda benzer doğruluk oranlarına ulaşsa da, genel olarak daha değişken bir performans sergilemiştir. Bununla birlikte, daha düşük hesaplama maliyeti ve daha hızlı işlem süresi sayesinde Pegasos-QSVM, özellikle kaynakların kısıtlı olduğu ortamlarda umut vadeden bir alternatif olarak öne çıkmaktadır. Bulgular, kuantum destekli modellerin klasik yaklaşımlarla rekabet edebilecek düzeyde performans gösterebildiğini ve özellikle QSVM’nin küçük ve orta ölçekli veri kümelerinde etkili olduğunu ortaya koymaktadır.

Keywords

• Pegasos-QSVM
• QSVM
• Tıbbi veri setleri
• Kuantum makine öğrenme

References

1. T.B. Alakus, M. Baykara, Comparison of Monkeypox and Wart DNA Sequences with Deep Learning Model, Applied Sciences 12 (2022) 10216. https://doi.org/10.3390/app122010216.
2. Ö. Yildirim, A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification, Computers in Biology and Medicine 96 (2018) 189–202. https://doi.org/10.1016/j.compbiomed.2018.03.016.
3. N. Jeyaraman, M. Jeyaraman, S. Yadav, S. Ramasubramanian, S. Balaji, Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment, Cureus (2024). https://doi.org/10.7759/cureus.67486.
4. R. Ur Rasool, H.F. Ahmad, W. Rafique, A. Qayyum, J. Qadir, Z. Anwar, Quantum Computing for Healthcare: A Review, Future Internet 15 (2023) 94. https://doi.org/10.3390/fi15030094.
5. Z. Li, Analysis of the Principles of Quantum Computing and State-of-the-Art Applications, Theoretical and Natural Science 41 (2024) 65–71. https://doi.org/10.54254/2753-8818/41/2024CH0155.
6. D. Dhinakaran, L. Srinivasan, S.M. Udhaya Sankar, D. Selvaraj, Quantum-based privacy-preserving techniques for secure and trustworthy internet of medical things an extensive analysis, QIC 24 (2024) 227–266. https://doi.org/10.26421/QIC24.3-4-3.
7. A.M. Dalzell, S. McArdle, M. Berta, P. Bienias, C.-F. Chen, A. Gilyén, C.T. Hann, M.J. Kastoryano, E.T. Khabiboulline, A. Kubica, G. Salton, S. Wang, F.G.S.L. Brandão, Quantum algorithms: A survey of applications and end-to-end complexities, (2023). https://doi.org/10.48550/arXiv.2310.03011.
8. T.M. Khan, A. Robles-Kelly, Machine Learning: Quantum vs Classical, IEEE Access 8 (2020) 219275–219294. https://doi.org/10.1109/ACCESS.2020.3041719.

9. P. Lamichhane, D.B. Rawat, Quantum Machine Learning: Recent Advances, Challenges, and Perspectives, IEEE Access 13 (2025) 94057–94105. https://doi.org/10.1109/ACCESS.2025.3573244.
10. V. Havlíček, A.D. Córcoles, K. Temme, A.W. Harrow, A. Kandala, J.M. Chow, J.M. Gambetta, Supervised learning with quantum-enhanced feature spaces, Nature 567 (2019) 209–212. https://doi.org/10.1038/s41586-019-0980-2.
11. R. Guido, S. Ferrisi, D. Lofaro, D. Conforti, An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review, Information 15 (2024) 235. https://doi.org/10.3390/info15040235.
12. A. Kodipalli, S. Devi, Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM, Front. Public Health 9 (2021). https://doi.org/10.3389/fpubh.2021.789569.
13. H.F. Kareem, M.S. AL-Huseiny, F.Y. Mohsen, E.A. Khalil, Z.S. Hassan, Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset, Indonesian Journal of Electrical Engineering and Computer Science 21 (2021) 1731–1738. https://doi.org/10.11591/ijeecs.v21.i3.pp1731-1738.
14. P.S. Oztekin, O. Katar, T. Omma, S. Erel, O. Tokur, D. Avci, M. Aydogan, O. Yildirim, E. Avci, U.R. Acharya, Comparison of Explainable Artificial Intelligence Model and Radiologist Review Performances to Detect Breast Cancer in 752 Patients, Journal of Ultrasound in Medicine 43 (2024) 2051–2068. https://doi.org/10.1002/jum.16535.
15. K. Ramu, S. Patthi, Y.N. Prajapati, J.V.N. Ramesh, S. Banerjee, K.B.V.B. Rao, S.I. Alzahrani, R. ayyasamy, Hybrid CNN-SVM model for enhanced early detection of Chronic kidney disease, Biomedical Signal Processing and Control 100 (2025) 107084. https://doi.org/10.1016/j.bspc.2024.107084.
16. A. Tudisco, D. Volpe, G. Turvani, Quantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models, (2025). https://doi.org/10.48550/arXiv.2505.20804.
17. R. Khushal, D.U. Fatima, Fuzzy quantum machine learning (FQML) logic for optimized disease prediction, Computers in Biology and Medicine 192 (2025) 110315. https://doi.org/10.1016/j.compbiomed.2025.110315.
18. D. Maheshwari, U. Ullah, P.A.O. Marulanda, A.G.-O. Jurado, I.D. Gonzalez, J.M.O. Merodio, B. Garcia-Zapirain, Quantum Machine Learning Applied to Electronic Healthcare Records for Ischemic Heart Disease Classification, Human-Centric Computing and Information Sciences 13 (2023) 1–15. https://doi.org/10.22967/HCIS.2023.13.006.
19. J. P, S. Hariharan, V. Madhivanan, S. N, M. Krisnamoorthy, A.K. Cherukuri, Enhanced QSVM with elitist non-dominated sorting genetic optimisation algorithm for breast cancer diagnosis, IET Quantum Communication 5 (2024) 384–398. https://doi.org/10.1049/qtc2.12113.
20. S. Chatterjee, A. Das, An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer, Soft Comput 27 (2023) 7147–7178. https://doi.org/10.1007/s00500-023-07939-x.
21. Y. Nasir, K. Kadian, V. Kumar, A. Wary, Harnessing Quantum Computing: A Comparative Study in Skin Disease Detection with Traditional ML, in: T. Senjyu, C. So–In, A. Joshi (Eds.), Smart Trends in Computing and Communications, Springer Nature, Singapore, 2024: pp. 361–370. https://doi.org/10.1007/978-981-97-1323-3_30.
22. N. Singh, S.R. Pokhrel, Modeling Quantum Machine Learning for Genomic Data Analysis, (2025). https://doi.org/10.48550/arXiv.2501.08193.
23. M. Munshi, R. Gupta, N.K. Jadav, S. Tanwar, A. Nair, D. Garg, Quantum Machine Learning-based Lung Cancer Prediction Framework for Healthcare 4.0, in: 2024 Asia Pacific Conference on Innovation in Technology (APCIT), 2024: pp. 1–6. https://doi.org/10.1109/APCIT62007.2024.10673456.
24. FidelityQuantumKernel - Qiskit Machine Learning 0.8.2, (n.d.). (accessed 15.06.2025).
25. J.P. Miguel Patrcio, Breast Cancer Coimbra, (2018). https://doi.org/10.24432/C52P59.
26. N.V. Bendi Ramana, ILPD (Indian Liver Patient Dataset), (2022). https://doi.org/10.24432/C5D02C.
27. Unknown, Heart Failure Clinical Records, (2020). https://doi.org/10.24432/C5Z89R.
28. I. Straw, H. Wu, Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction, BMJ Health Care Inform 29 (2022) e100457. https://doi.org/10.1136/bmjhci-2021-100457.
29. M. Patrício, J. Pereira, J. Crisóstomo, P. Matafome, M. Gomes, R. Seiça, F. Caramelo, Using Resistin, glucose, age and BMI to predict the presence of breast cancer, BMC Cancer 18 (2018) 29. https://doi.org/10.1186/s12885-017-3877-1.
30. D. Chicco, G. Jurman, Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone, BMC Med Inform Decis Mak 20 (2020) 16. https://doi.org/10.1186/s12911-020-1023-5.
31. I.T. Jolliffe, J. Cadima, Principal component analysis: a review and recent developments, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374 (2016) 20150202. https://doi.org/10.1098/rsta.2015.0202.
32. G.T. Reddy, M.P.K. Reddy, K. Lakshmanna, R. Kaluri, D.S. Rajput, G. Srivastava, T. Baker, Analysis of Dimensionality Reduction Techniques on Big Data, IEEE Access 8 (2020) 54776–54788. https://doi.org/10.1109/ACCESS.2020.2980942.
33. Z. Özpolat, Kuantum Tabanli Boyut İndirgeme ve Siniflandirici Gerçekleştirilmesi, MSc Thesis, Firat University, Elazig, Turkey, 2023.
34. M. Kaur, K. Jain, A. Singla, K. Kadian, Quantum Exploration in Ransomware Detection with Conventional Machine Learning Approaches, in: 2024 IEEE International Conference on Contemporary Computing and Communications (InC4), IEEE, 2024, pp. 1–8. https://doi.org/10.1109/InC460750.2024.10649082.
35. V.N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, 2000. https://doi.org/10.1007/978-1-4757-3264-1.
36. K.C. Chua, V. Chandran, U.R. Acharya, C.M. Lim, Application of Higher Order Spectra to Identify Epileptic EEG, Journal of Medical Systems 35 (2011) 1563–1571. https://doi.org/10.1007/s10916-010-9433-z.
37. D. Anguita, S. Ridella, F. Rivieccio, R. Zunino, Quantum optimization for training support vector machines, Neural Networks 16 (2003) 763–770. https://doi.org/10.1016/S0893-6080(03)00087-X.
38. A. Zeguendry, Z. Jarir, M. Quafafou, Quantum Machine Learning: A Review and Case Studies, Entropy 25 (2023) 287. https://doi.org/10.3390/e25020287.
39. M. Aly, S. Fadaaq, O.A. Warga, Q. Nasir, M.A. Talib, Experimental Benchmarking of Quantum Machine Learning Classifiers, in: 2023 6th International Conference on Signal Processing and Information Security (ICSPIS), 2023: pp. 240–245. https://doi.org/10.1109/ICSPIS60075.2023.10343811.
40. A. Thomsen, Comparing Quantum Neural Networks and Quantum Support Vector Machines, MSc Thesis, ETH Zurich, 2021, 97 p. https://doi.org/10.3929/ETHZ-B-000527559.
41. S. Altares-López, A. Ribeiro, J.J. García-Ripoll, Automatic design of quantum feature maps, Quantum Sci. Technol. 6 (2021) 045015. https://doi.org/10.1088/2058-9565/ac1ab1.
42. A. Daspal, OptiPauli: An algorithm to find a near-optimal Pauli Feature Map for Quantum Support Vector Classifiers, in: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), 2022: pp. 828–830. https://doi.org/10.1109/QCE53715.2022.00133.