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Untangling the complexity of diabetes risk: a Bayesian approach to learning causal structures

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Model validation study demonstrates high accuracy of bayesian networks in diabetes detection, highlighting causal structures' implications.

Bayesian networks achieved over 95% accuracy in predicting diabetes risk, showing high effectiveness.
The FSSJ model outperformed others, reaching an accuracy of 97% and a specificity of 1.00.
Analysis revealed causal structures involving body mass index, glucose, and hypertension, reinforcing clinical relevance.
Basic models performed comparably to complex ones, indicating effective preprocessing simplifies diabetes risk assessment.

Ney Michel Lituma Villamar (2025) studied this question.

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Model validation study demonstrates high accuracy of bayesian networks in diabetes detection, highlighting causal structures' implications.