Statistical analysis
HC
REGRESSIONS
The available data appears to be appropriate for achieving research goals; however, some methodological considerations are necessary to address the complexity of the design. To simplify the relationships between variables, we used the BN method, one of the most well-known algorithms in data mining.The statistical method employed to evaluate the generalizability of our findings was a tenfold cross-validation technique. The dataset was randomly divided into ten equal parts, with nine parts being utilized for model training in each iteration. The remaining part was used for evaluating the predicted values. To obtain a comprehensive performance evaluation, we calculated the sensitivity, specificity, and accuracy metrics by averaging these values across all ten iterations.We employed the Hill-Climbing (HC) algorithm to achieve the best Directed Acyclic Graph (DAG) corresponding to our data. HC is a score-based structure learning algorithm that progressively restricts the neighborhood. Compared with constraint-based algorithms, score-based algorithms result in entirely directed DAG, which is their primary advantageTo fit the parameters of BN corresponding to DAG achieved in structure learning, we used the Bayesian estimationFinally, we fitted several logistic regressions adjusted for potential confounders as the alternative approach for modeling the data. We compared the prediction power of these models with the suggested BN using the AUC of these models in the test datasets.The statistical analysis, data preparation, and graphical representation were conducted using the R statistical software environment. We used the "bnlearn" package to fit the BN model