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Updated: Apr 15, 2026

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

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Published on: June 23, 2023

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

Azad Asaf¹, Yavuz Selim Ogur², Ayşe Erdoğan Kaya³

¹Department of Child and Adolescent Psychiatry, Hitit University Çorum Erol Olçok Education and Research Hospital, Çorum 19040, Türkiye.

Healthcare (Basel, Switzerland)

|April 14, 2026

Summary

This summary is machine-generated.

Machine learning models analyzing routine hemogram parameters show promise in differentiating alcohol and substance use disorders. While effective, further validation is needed for clinical use.

Keywords:

alcohol dependence biomarker hematological parameters machine learning substance dependence

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Area of Science:

Hematology
Machine Learning
Addiction Medicine

Background:

Clinical evaluation is standard for diagnosing alcohol and substance use disorders.
Objective biomarkers for these conditions are lacking, posing a diagnostic challenge.

Purpose of the Study:

To investigate the utility of routine hemogram parameters in differentiating alcohol use disorder (AUD), substance use disorder (SUD), and healthy controls.
To apply multiclass machine learning models for this classification task.

Main Methods:

Retrospective case-control study involving 35 AUD patients, 61 SUD patients, and 132 controls.
Analysis of routine hematological parameters using Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) models.
10-fold cross-validation for performance evaluation using accuracy, sensitivity, precision, F1-score, and AUC.

Main Results:

Significant differences in monocyte count, basophil count, and RDW-CV were observed between groups.
The Random Forest model achieved the highest accuracy (81.6%) and AUC (0.93).
Classification performance varied, with lower sensitivity for the alcohol use disorder group.

Conclusions:

Routine hemogram parameters analyzed by machine learning offer a potential low-cost, accessible supportive tool for differentiating addiction-related conditions.
Findings are exploratory due to study limitations (retrospective, single-center, small sample size, lack of external validation).
Further research with larger datasets and explainable AI is necessary for clinical application.