Investigating the validity of structure learning algorithms in
  identifying risk factors for intervention in patients with diabetes
Sheresh Zahoor Anthony C. Constantinou Tim M Curtis Mohammed Hasanuzzaman
Diabetes, a pervasive and enduring health challenge, imposes significant
global implications on health, financial healthcare systems, and societal
well-being. This study undertakes a comprehensive exploration of various
structural learning algorithms to discern causal pathways amongst potential
risk factors influencing diabetes progression. The methodology involves the
application of these algorithms to relevant diabetes data, followed by the
conversion of their output graphs into Causal Bayesian Networks (CBNs),
enabling predictive analysis and the evaluation of discrepancies in the effect
of hypothetical interventions within our context-specific case study.
  This study highlights the substantial impact of algorithm selection on
intervention outcomes. To consolidate insights from diverse algorithms, we
employ a model-averaging technique that helps us obtain a unique causal model
for diabetes derived from a varied set of structural learning algorithms. We
also investigate how each of those individual graphs, as well as the average
graph, compare to the structures elicited by a domain expert who categorised
graph edges into high confidence, moderate, and low confidence types, leading
into three individual graphs corresponding to the three levels of confidence.
  The resulting causal model and data are made available online, and serve as a
valuable resource and a guide for informed decision-making by healthcare
practitioners, offering a comprehensive understanding of the interactions
between relevant risk factors and the effect of hypothetical interventions.
Therefore, this research not only contributes to the academic discussion on
diabetes, but also provides practical guidance for healthcare professionals in
developing efficient intervention and risk management strategies.