Comparative performance analysis of Machine Learning with Quantum Machine Learning for breast cancer prediction

Mulani, Sahil (2024) Comparative performance analysis of Machine Learning with Quantum Machine Learning for breast cancer prediction. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the public health sector, a well-known heterogeneous disease whose incident rate has seen a sharp increase is breast cancer. It is a widely known cause of mortality among women. However, detecting breast cancer in the initial stages can increase survival chances and save lives for many people. The primary focus of this research is to develop a prediction tool using the Wisconsin Breast Cancer (Diagnostic) data set that would help medical practitioners diagnose breast cancer in the early stages. We have selected two prediction models, Classical Machine Learning models (Machine Learning & Deep Learning) and Quantum Machine Learning (QML) models based on the features of the data set. The performance of the models was evaluated on the basis of sensitivity, i.e. true positive rate. The results indicate that the Classical ML models outperformed the QML models, with ANN achieving the highest sensitivity of 98.14% followed by Random Forest with a sensitivity of 94.51%. The QML models gave satisfactory results, achieving the maximum sensitivity of 82.85%; however, its performance was limited due to hardware constraints.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Stynes, Paul UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Life sciences > Medical sciences > Pathology > Tumors > Cancer Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Q Science > QA Mathematics > Electronic computers. Computer science > Computer Systems > Computers > Electronic data processing > Quantum computing T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science > Computer Systems > Computers > Electronic data processing > Quantum computing
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	20 Aug 2025 11:36
Last Modified:	20 Aug 2025 11:36
URI:	https://norma.ncirl.ie/id/eprint/8594

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