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Network Engineering Research Operation

IMPLEMENTASI QSVM DALAM KLASIFIKASI BINER PADA KASUS KANKER PROSTAT

Nur Amalina Rahmaputri Hilmy, Muhamad Akrom

Published Nov 30, 2024• 0 citations
Publisher

Abstract

Quantum Machine Learning (QML) is increasingly attracting attention as a potential solution to improve computational performance, especially in handling complex and big data-driven classification tasks. In this study, the Quantum Support Vector Machine (QSVM) algorithm is applied to prostate cancer classification, with the results compared to the classical Support Vector Machine (SVM) model. QSVM shows superiority in accuracy, reaching 0.93, compared to the classical SVM which has an accuracy of 0.91. In addition, QSVM produces precision, recall, and F1-score values of 0.83, 0.95, and 0.88, respectively, higher than the precision of 0.82, recall of 0.93, and F1-score of 0.87 of the classical SVM. These findings indicate that QSVM is more effective in handling high-dimensional data and complex classification, thus demonstrating the great potential of QML in medical applications, especially in cancer classification and biomarker discovery.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Klasifikasi, Kanker Prostat

DOI
10.21107/nero.v9i2.27781

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Quantum Machine Learning (QML) is increasingly attracting attention as a potential solution to improve computational performance, especially in handling complex and big data-driven classification tasks. In this study, the Quantum Support Vector Machine (QSVM) algorithm is applied to prostate cancer classification, with the results compared to the classical Support Vector Machine (SVM) model. QSVM shows superiority in accuracy, reaching 0.93, compared to the classical SVM which has an accuracy of 0.91. In addition, QSVM produces precision, recall, and F1-score values of 0.83, 0.95, and 0.88, respectively, higher than the precision of 0.82, recall of 0.93, and F1-score of 0.87 of the classical SVM. These findings indicate that QSVM is more effective in handling high-dimensional data and complex classification, thus demonstrating the great potential of QML in medical applications, especially in cancer classification and biomarker discovery.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Klasifikasi, Kanker Prostat
This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approach. Our study not only outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique, but it delves into the advancements brought about by the quantum feature map architecture, which has been carefully identified and evaluated, ensuring it aligns seamlessly with the unique characteristics of our prostate cancer dataset. This architecture succeded in creating a distinct feature space, enabling the detection of complex, non-linear patterns in the data. The findings reveal not only a comparable accuracy with classical SVM (92%92\%92%) but also a 7.14%7.14\%7.14% increase in sensitivity and a notably high F1-Score (93.33%93.33\%93.33%). This study's important combination of quantum computing in medical diagnostics marks a pivotal step forward in cancer detection, offering promising implications for the future of healthcare technology.
Abstract In this study, we compare the performance of classical Support Vector Machines (SVM) and Quantum Support Vector Machines (QSVM) on binary classification tasks, using 149 balanced datasets derived from a lung cancer diagnosis dataset. Each dataset consists of 120 samples and 12 features. Our primary objective is to evaluate whether QSVM provides measurable advantages over SVM, particularly in low-performing scenarios. To this end, we identified the 15 datasets where classical SVM exhibited the lowest F1-scores and conducted a focused comparative analysis. Results show that, in these challenging cases, QSVM achieved an average recall improvement of 99% over SVM, without compromising precision. This substantial gain in recall also led to a corresponding 48% increase in F1-score, on average. Statistical analysis using a normality test followed by a paired t-test confirmed the significance of these results. These findings suggest that QSVM can serve as a valuable alternative in situations where classical models struggle, especially when high recall is critical for early cancer detection.
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Abstract This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approach. Our study not only outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique, but it delves into the advancements brought about by the quantum feature map architecture, which has been carefully identified and evaluated, ensuring it aligns seamlessly with the unique characteristics of our prostate cancer dataset. This architecture succeded in creating a distinct feature space, enabling the detection of complex, non-linear patterns in the data. The findings reveal not only a comparable accuracy with classical SVM (92%) but also a 7.14% increase in sensitivity and a notably high F1-Score (93.33%). This study's important combination of quantum computing in medical diagnostics marks a pivotal step forward in cancer detection, offering promising implications for the future of healthcare technology.
Quantum support vector machines (QSVMs) are a novel approach for solving classification problems, combining the principles of quantum computing with the support vector machine algorithm. In this study, we applied a QSVM to the Breast Cancer Wisconsin (Original) data set to classify tumors as benign or malignant. Our results showed that the QSVM outperformed classical support vector machines in terms of classification accuracy, achieving a success rate of 100% while classical SVM shows almost perfect result with its accuracy 95%. Additionally, the QSVM required significantly less training time compared to the classical SVM. These results demonstrate the potential of QSVMs for solving complex classification problems in the field of medical diagnosis.
This study analyzes the comparative performance of Quantum Machine Learning in classifying factual and hoax political news using three approaches, namely Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), and Hybrid Quantum Kernel with Classical SVM. News data is represented using TF-IDF and its dimensionality is reduced using Principal Component Analysis, then balanced using SMOTE. Feature transformation is carried out to the quantum domain through angle encoding, then applied to the QML model. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score. The experimental results show that QSVC has the best performance with an accuracy of 0.629 and an F1-Score of 0.735, followed by QNN and Hybrid Quantum Kernel Classical SVM. This study proves that the quantum kernel-based approach is effective in classifying medium-dimensional text, while also demonstrating the potential of Quantum Machine Learning as an alternative method for classifying factual and hoax political news.Keywords: Quantum Machine Learning; Quantum Neural Network; Quantum Support Vector Classifier; Hybrid Quantum Kernel; News Classification AbstrakPenelitian ini menganalisis perbandingan kinerja Quantum Machine Learning dalam klasifikasi berita politik fakta dan hoaks dengan menggunakan tiga pendekatan, yaitu Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), dan Hybrid Quantum Kernel dengan Classical SVM. Data berita direpresentasikan menggunakan TF-IDF dan direduksi dimensinya dengan Principal Component Analysis, kemudian diseimbangkan menggunakan SMOTE. Transformasi fitur dilakukan ke domain kuantum melalui angle encoding, kemudian diterapkan pada model QML. Evaluasi kinerja dilakukan menggunakan accuracy, precision, recall, dan F1-Score. Hasil eksperimen menunjukkan QSVC memiliki performa terbaik dengan accuracy 0,629 dan F1-Score 0,735, diikuti QNN dan Hybrid Quantum Kernel Classical SVM. Penelitian ini membuktikan bahwa pendekatan berbasis quantum kernel efektif dalam klasifikasi teks berdimensi sedang, sekaligus menunjukkan potensi Quantum Machine Learning sebagai alternatif metode klasifikasi berita politik fakta dan hoaks.