Quantum Neural Networks for Enhanced Predictive Analytics in Cancer Prognosis
Sukhdev Singh, Chaitanya Singla, Nilesh M Shelke +1 more
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.
International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)•Sep 18, 2025•6 citations