Abstract: Existing deep learning-based disease prediction models are predominantly datadriven, leading to a high dependency on the sample size and the coverage of disease types in the training dataset. The current methods for disease prediction have the following limitations: 1) When the model is trained on a limited range of disease types, its performance deteriorates significantly and may produce incorrect predictions for rare diseases. 2) The training data may contain features that are irrelevant or have weak correlations with the prediction target. This noise may prevent the model from making stable and reliable predictions, thus failing to meet the practical needs of high safety and reliability required in medical applications. To address these issues, this paper proposes a disease prediction model named CausalCap, which integrates capsule networks with causal inference. Specifically, we obtain the causal effects and relationships between clinical features and disease labels, and construct a causal graph of clinical features. The causal graph is then pruned to delete false nodes with no causal relationships to the disease labels, only retaining key nodes that truly influence the occurrence of the disease, resulting in a refined disease causal graph. Finally, hierarchical graph capsule neural network (HGCN) classifies the disease causal graph for disease prediction. Extensive evaluations on six public datasets demonstrate that CausalCap achieves an average improvement of 2.50% in ACC and 6.46% in F1 metrics compared to the suboptimal methods.

Key words: disease prediction, causal inference, capsule network, dynamic routing, graph classification