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Abstract <p>The accurate classification of Chagas disease stages based on <i>Trypanosoma cruzi</i> infection in experimental models faces challenges due to the scarcity of data and the high dimensionality of diagnostic sources. This study proposes an architecture based on Graph Attention Networks on fully connected graphs to combine multimodal features (Electrocardiogram, Echocardiogram, Doppler, and ELISA). A Variational Graph Autoencoder was used to implement a data augmentation strategy that reduces overfitting and optimizes generalization, producing synthetic subjects with a biological covariance structure. The results demonstrate that the proposed methodology is effective compared to state-of-the-art classifiers, achieving an AUROC of 100% in the identification of infection stage through multimodal fusion and significantly optimizing performance in complex modalities such as Doppler. The pathophysiological consistency of the model’s decisions was also validated using an interpretability scheme (GNNExplainer), which allowed the detection of important clinical features and established GATs as a robust method in the classification of <i>Trypanosoma cruzi</i> infection stages.</p> Graphical abstract <p></p>

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Multimodal graph learning for chagas disease classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Gabriel Carcedo-Rodríguez,
  • Erik Molino-Minero-Re,
  • Jorge Perez-Gonzalez,
  • Nidiyare Hevia-Montiel

摘要

Abstract

The accurate classification of Chagas disease stages based on Trypanosoma cruzi infection in experimental models faces challenges due to the scarcity of data and the high dimensionality of diagnostic sources. This study proposes an architecture based on Graph Attention Networks on fully connected graphs to combine multimodal features (Electrocardiogram, Echocardiogram, Doppler, and ELISA). A Variational Graph Autoencoder was used to implement a data augmentation strategy that reduces overfitting and optimizes generalization, producing synthetic subjects with a biological covariance structure. The results demonstrate that the proposed methodology is effective compared to state-of-the-art classifiers, achieving an AUROC of 100% in the identification of infection stage through multimodal fusion and significantly optimizing performance in complex modalities such as Doppler. The pathophysiological consistency of the model’s decisions was also validated using an interpretability scheme (GNNExplainer), which allowed the detection of important clinical features and established GATs as a robust method in the classification of Trypanosoma cruzi infection stages.

Graphical abstract