Quantum Neural Networks for Enhanced Predictive Analytics in Cancer Prognosis

Abstract

Cancer prognosis prediction faces challenges such as complex multimodal data and computational inefficiencies, leading to delayed clinical decisions. This paper proposes a novel hybrid quantum-classical neural network (HQNN) framework for enhanced predictive analytics in oncology. By integrating quantum variational circuits with classical convolutional neural networks (CNN) and real-time multimodal data (genomic and imaging), the framework achieves classification accuracy of 92. 5 %, outperforming traditional CNNs (80.5 %) and deep neural networks (DNNs, 85.5%) in the TCGA dataset. The methodology includes data preprocessing, quantum feature encoding, and real-time prediction. Tested on 10,340 samples across 33 cancer types, the HQNN reduces inference time by 20 % and improves accuracy by 12 % over baselines. This work underscores the potential of QNNs to transform precision oncology, enabling faster and more accurate prognosis predictions to support clinical decision-making.

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