AUTHOR=Melaku Mequannent Sharew , Yohannes Lamrot , Taye Eliyas Addisu , Demis Baykemagn Nebebe 

TITLE=Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and Health Survey

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1555697

DOI=10.3389/fpubh.2025.1555697

ISSN=2296-2565

ABSTRACT=IntroductionKhat chewing is a significant public health issue in Ethiopia, influenced by various demographic factors. Understanding the prevalence and determinants of khat chewing practices is essential to developing targeted interventions. Therefore, this study aimed to predict khat chewing practices and their determinant factors among men aged 15 to 59 years in Ethiopia using a machine learning algorithm.MethodsThis study used data from the 2011 and 2016 Ethiopian Demographic and Health Surveys (EDHS). A weighted sample of 26,798 men aged 15 to 59 years was included in the study. STATA version 17 was used for data cleaning, weighting, and descriptive statistical analysis. Python 3.12 software was used for machine learning-based predictions of khat chewing among men. Furthermore, Decision Tree, Logistic Regression, Random Forest, KNN, Support Vector Machine, eXtreme Gradient Boosting (XGBoost), and AdaBoost classifiers were employed to identify the most critical predictors of khat chewing practices among men. In addition, accuracy and the area under the curve were used to evaluate the performance of predictive models.ResultFrom a total weighted sample of 26,798 men, 8,786 men (32.79) aged 15 to 59 years reported chewing khat. The eXtreme Gradient Boosting (XGBoost) model demonstrated the highest predictive accuracy at 87%, with an area under the ROC curve (AUC) of 0.94. The Beeswarm plot from the SHAP analysis (based on the XGBoost classifier model) identified the top-ranked variables for predicting khat chewing among men, including age, religion, region, wealth index, age at first sexual encounter, frequency of watching television, frequency of listening to the radio, and number of sexual partners.ConclusionOverall, three in 10 men in Ethiopia chew khat. The XGBoost model demonstrated superior predictive performance in identifying the determinants of khat chewing practices. This model identified age, religion, region, wealth index, age at first sexual encounter, media exposure, and the number of sexual partners as key predictors of khat chewing among Ethiopian men. Effective khat prevention strategies should focus on the following: preserving rural norms that discourage khat use and expanding these to urban areas; targeted interventions for young and middle-aged men, including youth programs and economic empowerment initiatives as alternative opportunities; strengthening family values through marriage counseling and spouse involvement to help reduce khat chewing; integrating khat education into reproductive health programs and engaging religious leaders in awareness efforts; and, finally, implementing media campaigns, school-based education, and policy measures—such as restricting sales near schools and enforcing community bylaws—to further curb khat consumption while promoting healthier economic alternatives.