Diabetes Bayesian Network: A Probabilistic Model with 413 Nodes

Diabetes_1 is a discrete Bayesian network model for diabetes, referenced from the bnlearn repository. The model contains 413 nodes connected by 602 arcs, with 429,409 parameters. It was authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson, with a related paper published in 1991.

Use Cases

• Probabilistic inference for diabetes-related variables based on the network's 413 nodes.
• Causal reasoning and sensitivity analysis based on the model's 602 directed arcs.
• Benchmarking structure learning algorithms based on the known network topology.
• Developing clinical decision support systems based on the model's parameters for insulin adjustment.

Strengths

• The network structure is explicitly defined with 413 nodes and 602 arcs.
• Model complexity is quantified with 429,409 parameters and an average Markov blanket size of 3.97.
• Authorship and a citable academic source from 1991 are provided.

Limitations

• Column-level documentation and sample data are unavailable; field semantics must be inferred after download.
• Row count, file formats, and data size are unknown, limiting suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson.

Collection Method

Likely a model-based approach to insulin adjustment, as described in the cited 1991 conference paper.

Time Range

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Freshness

Last updated date is unknown.

Geography

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