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Predicting mental health disparities using machine learning for African Americans in Southeastern Virginia
Ismail El Moudden1, Michael C Bittner1, Matvey V Karpov1
1Eastern Virginia Medical School (EVMS), Norfolk State University, Norfolk, VA, USA.
Scientific Reports
|February 18, 2025
Summary
This study highlights significant mental health disparities in African Americans, with mood disorders being most common. AI models identified key risk factors like gender and age, indicating a higher burden in Southeastern Virginia.
Area of Science:
- Public Health
- Artificial Intelligence in Healthcare
- Mental Health Research
Background:
- African Americans face significant mental health disparities.
- AI and machine learning offer novel approaches for analyzing health outcome data.
- Understanding demographic and clinical risk factors is crucial for targeted interventions.
Purpose of the Study:
- To examine mental health disparities among African Americans in Southeastern Virginia.
- To predict mental health disorder outcomes using AI and machine learning.
- To identify key demographic and clinical predictors of mental health disorders in this population.
Main Methods:
- Analysis of retrospective data from African American adults (18-85) in Southeastern Virginia (2016-2020).
- Application and validation of various machine learning models (gradient boosting, random forest, neural networks, logistic regression, Naive Bayes) using 100 repeated 5-fold cross-validations.
- Development of nomograms to visualize risk factors.
Main Results:
- Mood Affective Disorders (41.66%) and Schizophrenia Spectrum and Other Psychotic Disorders were most prevalent.
- Females predominantly experienced mood disorders; ages 30s-40s were common.
- Gradient boosting showed superior predictive performance; gender, age, comorbidities, and insurance type were key predictors.
- Higher mental health disorder prevalence was observed compared to national averages.
Conclusions:
- African Americans in Southeastern Virginia exhibit a potentially greater mental health burden.
- AI and machine learning models effectively predict mental health outcomes and identify risk factors.
- Findings underscore the need for targeted interventions addressing mental health disparities in this demographic.
Keywords:
African American populationsComorbidity AnalysisHealthcare AccessibilityMachine learning in HealthcareMental Health disparitiesPredictive modelingMore Related Videos
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