Differentiating Alcohol and Substance Use Disorders Using Multiclass Machine Learning Models Based on Routine Hemogram Parameters

These findings suggest that machine learning models based on routine hemogram parameters may provide a preliminary, low-cost, and accessible supportive approach for differentiating addiction-related conditions. However, the results should be interpreted as exploratory, given the retrospective single-center design, limited sample size, lack of external validation, and absence of model interpretability analyses. Further studies incorporating larger multicenter datasets, confounding factors, and explainable artificial intelligence methods are required before clinical application can be considered.