A machine learning model for the prediction of unhealthy alcohol use among women of childbearing age in Alabama.

Abstract

This study utilizes a machine learning model to predict unhealthy alcohol use treatment levels among women of childbearing age. In this cross-sectional study, women of childbearing age (n = 2397) were screened for alcohol use over a 2-year period as part of the AL-SBIRT (screening, brief intervention, and referral to treatment in Alabama) program in three healthcare settings across Alabama for unhealthy alcohol use severity and depression. A support vector machine learning model was estimated to predict unhealthy alcohol use scores based on depression score and age. The machine learning model was effective in predicting no intervention among patients with lower Patient Health Questionnaire (PHQ)-2 scores of any age, but a brief intervention among younger patients (aged 18-27years) with PHQ-2 scores >3 and a referral to treatment for unhealthy alcohol use among older patients (between the ages of 25 and 50) with PHQ-2 scores >4. The machine learning model can be an effective tool in predicting unhealthy alcohol use treatment levels and approaches.