Diabetes Bayesian Network: A Discrete Model with 413 Nodes

413 nodes and 602 arcs define this discrete Bayesian network for modeling diabetes. The model, authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson, was presented in a 1991 conference on artificial intelligence in medicine. It contains 429,409 parameters and has an average Markov blanket size of 3.97.

Use Cases

• Probabilistic inference for diabetes patient state based on the network's conditional dependencies.
• Testing and benchmarking Bayesian network learning algorithms based on the provided structure and parameter count.
• Simulating clinical scenarios for insulin adjustment based on the model's medical domain focus.
• Educational demonstrations of large-scale discrete Bayesian networks based on the detailed topological statistics.

Strengths

• The network structure is explicitly defined with 413 nodes and 602 arcs.
• Contains a substantial number of parameters (429,409) for detailed probabilistic modeling.
• Has a clear academic provenance, being cited from a peer-reviewed 1991 conference proceeding.

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

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

Collection Method

Model-based approach, likely derived from medical knowledge and data.

Time Range

null

Freshness

unknown

Geography

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