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

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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