Diabetes Bayesian Network with 413 Nodes and 602 Arcs

413 nodes and 602 arcs define this Bayesian network modeling diabetes. The network contains 429,409 parameters and was authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson for a model-based approach to insulin adjustment, published in 1991.

Use Cases

• Probabilistic inference for diabetes-related variables based on the network's conditional dependencies.
• Evaluating and benchmarking Bayesian network structure learning algorithms against a known medical domain model.
• Studying the application of graphical models for clinical decision support in insulin adjustment.

Strengths

• The network structure is explicitly defined with 413 nodes and 602 arcs, providing a detailed model.
• The model is grounded in published medical research from 1991, offering a real-world application context.
• Key network statistics, such as an average Markov blanket size of 3.97, are provided for analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain machine learning tasks.
• Last update date is unknown; freshness unverified.

Provenance

Source

bnlearn Bayesian Network Repository, originally from research by S. Andreassen et al.

Collection Method

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Time Range

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Freshness

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Geography

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