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This research aims to uncover the causal relationships among risk factors for type 2 diabetes mellitus using a new method.

August 2, 2026PeerJ Computer ScienceOpen Access

Graph autoencoder based causal discovery method for type 2 diabetes mellitus risk factors

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This research aims to uncover the causal relationships among risk factors for type 2 diabetes mellitus using a new method.
Developed a causal discovery method using Graph Autoencoder (GAE) tailored for T2DM risk factors.
Constructed a candidate space for nonlinear causal structures through a data generation model.
Optimized the causal structure by minimizing GAE reconstruction error and validating with datasets.
Achieved 80% accuracy on a dataset with a known true Directed Acyclic Graph (DAG).
Obtained an average plausibility rate of 82.8% on three diabetes datasets, outperforming comparative methods.
The proposed method revealed more potential causal relationships for T2DM risk factors.

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Randomized trial examines causal relationships among T2DM risk factors, indicating new prevention strategies.

Chen et al. (2026) studied this question.

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