Abstract: Objective To develop a multi-cancer risk prediction model using data from 15 common cancers in the UK Biobank, employing a multi-modal data fusion approach, so as to explore the application of genomic and clinical data in cancer risk prediction, with the goal of enhancing early cancer detection accuracy and providing valuable insights for personalized medicine. Methods The rigorous quality control was performed to the data. High-dimensional genomic data were then transformed into image representations and processed using convolutional neural networks, while clinical data were modeled using multi-layer perceptron. An attention mechanism was incorporated to perform weighted fusion of features from both genomic and clinical modalities, aiming to optimize predictive performance. Results The integration of genomic and clinical data through a multi-modal fusion model resulted in a significant improvement in cancer prediction accuracy. Features extracted by convolutional neural networks from genomic data and by multi-layer perceptron from clinical data effectively augmented the predictive capability of the model, enhancing both the accuracy and robustness of the predictions. Conclusion This study introduces a novel multi-cancer risk prediction framework that integrates genomic and clinical data. The application of multi-modal deep learning techniques, including convolutional neural networks, multi-layer perceptrons, and attention mechanisms, significantly enhances early cancer prediction accuracy. The findings provide robust early support for cancer diagnosis and personalized treatment strategies, demonstrating the potential of multi-modal approaches in precision oncology.

Key words: Multi-cancer risk prediction, Multi-modal data, Genomics, Clinical data, Convolutional neural networks, Multi-layer perceptron