Authors: Mr.S.K. Sankar, Janga Sanjay, Didde Vaishali, Dwarampudi Tejo Madhuri, Chitturi Nikhitha
Abstract: Early detection of cancer plays a crucial role in improving patient survival rates and enabling effective treatment strategies. However, traditional diagnostic methods often face challenges such as high-dimensional biomedical data, feature redundancy, and computational inefficiency. Recent advancements in machine learning have improved diagnostic capabilities, but conventional algorithms still struggle to efficiently process complex genomic and medical imaging datasets. To address these limitations, this study proposes a novel framework that integrates quantum computing with machine learning techniques for enhanced cancer detection. The proposed approach employs a sequence of intelligent modules including Quantum-Normalized Adaptive Refinement (Q-NAR) for data preprocessing, Wrapper Component Attribute Analysis (WCAA) for feature ranking, and Swing L-Bee Mustard Optimization (SLBMO) for selecting the most relevant features. Finally, a hybrid predictive model known as the Quantum Boosted Vector Fusion Network (QBVFN) is utilized to perform cancer prediction and treatment outcome analysis. The framework is evaluated using the Cancer Genome Atlas (TCGA) dataset in a Python environment. Experimental results demonstrate significant improvements in feature optimization, prediction accuracy, and computational efficiency for early-stage cancer detection. This research highlights the potential of quantum-assisted machine learning techniques to support next-generation intelligent cancer diagnostic systems.
DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.139
