Leveraging Quantum Machine Learning for Early Ovarian Cancer Diagnosis

• Ukange N. SYBIL

  Department of Computer Science, Bayero University, Kano, Nigeria

  Author

• Hadiza A. UMAR

  Department of Computer Science, Bayero University, Kano, Nigeria

  Author

• Ogar M. OKO

  Department of Computer Science, Federal University, Wukari, Nigeria

  Author

• Habeebah A. KAKUDI

  Department of Computer Science, Bayero University, Kano, Nigeria

  Author

• Usman MAHMUD

  Department of Software Engineering, Northwest University, Kano, Kano State, Nigeria

  Author

• Alex AARON

  Department of Computer Science, Bayero University, Kano, Nigeria

  Author

Ovarian cancer remains one of the leading causes of cancer-related mortality among women worldwide, largely because most cases are diagnosed at advanced stages. Current diagnostic tools and classical machine learning models often show limited sensitivity and specificity, particularly when applied to the complex, high-dimensional datasets required for accurate prediction. These limitations highlight the need for more powerful computational approaches capable of extracting subtle diagnostic patterns. The aim of this study is to enhance the accuracy and efficiency of ovarian cancer diagnosis by using quantum-inspired machine learning approach such as Quantum Support Vector Machines (QSVMs).  The study utilized QSVMs to analyse clinical and genomic datasets, leveraging quantum mechanics principles like superposition and entanglement to process data more effectively than traditional machine learning models. The QSVM model was developed, trained, and evaluated using performance metrics such as accuracy, sensitivity, specificity, and processing efficiency. The results demonstrated that QSVMs achieved a diagnostic accuracy of 92%, outperforming traditional support vector machines and other classical models. Additionally, the processing time for diagnosis was reduced from 45 minutes to 20 minutes, providing a faster and more reliable workflow. The QSVMs also excelled in analysing multi-omics data, enabling the identification of early-stage ovarian cancer biomarkers and supporting personalized treatment strategies. In conclusion, this study demonstrates the potential of QSVMs to transform ovarian cancer diagnostics by addressing key limitations of conventional methods. The findings underscore the importance of adopting quantum-inspired machine learning in medical applications and encourage further exploration of these advanced algorithms in improving healthcare outcomes.

• FJET_12_82_75

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