Explainable Multimodal Fusion of Genomic and Clinical Data for Multi-Disease Prediction: A Deep Learning Approach

Abstract

Precision medicine is an effort to customize healthcare treatment based on individual-specific genetic, clinical, and environmental traits. This study introduces an explainable AI platform to fuse genomic and clinical information to enhance disease prediction and individualized treatment regimens. Pre-processed, normalized, and multi-modal datasets consisting of whole-genome sequencing, gene expression data, and electronic health records were integrated through a hybrid data fusion process. Feature engineering and dimensionality reduction techniques were utilized to discover biological and clinical significant patterns, which was followed by meticulous training of a multi-layer neural network for prediction. Explainability was also coupled with SHAP and Layer-wise Relevance Propagation for discovering the most influential genomic and clinical features that drive model decision-making. The results indicate the superiority of the joint model over the single modality model across all disease prediction tasks, with improved accuracy, precision, recall, and F1-scores. Feature importance analysis revealed important genomic variants and clinical predictors influencing predictions, enhancing model interpretability. These findings demonstrate the potential of explainable AI to integrate genomic and clinical data to support improved diagnosis, guide tailored therapies, and establish trust in AI-based clinical decision-making, resulting in real-world application in precision medicine.