Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes

arXiv:2403.14327v1 Announce Type: new
Abstract: 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 …

abstract algorithms arxiv causal challenge cs.lg diabetes exploration financial global health healthcare patients risk study systems type