ORCID

0000-0001-7702-2564 (Moudden), 0000-0001-7089-5266 (Karpov)

Document Type

Article

Publication Date

2025

DOI

10.1038/s41598-025-89579-9

Publication Title

Scientific Reports

Volume

15

Issue

1

Pages

5900

Abstract

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18–85) in Southeastern Virginia (2016–2020), we found Mood Affective Disorders were most prevalent (41.66%), followed by Schizophrenia Spectrum and Other Psychotic Disorders. Females predominantly experienced mood disorders, with patient ages typically ranging from late thirties to mid-forties. Medicare coverage was notably high among schizophrenia patients, while emergency admissions and comorbidities significantly impacted total healthcare charges. Machine learning models, including gradient boosting, random forest, neural networks, logistic regression, and Naive Bayes, were validated through 100 repeated 5-fold cross-validations. Gradient boosting demonstrated superior predictive performance among all models. Nomograms were developed to visualize risk factors, with gender, age, comorbidities, and insurance type emerging as key predictors. The study revealed higher mental health disorder prevalence compared to national averages, suggesting a potentially greater mental health burden in this population. Despite the limitations of its retrospective design and regional focus, this research provides valuable insights into mental health disparities among African Americans in Southeastern Virginia, particularly regarding demographic and clinical risk factors.

Rights

© 2025 The Authors.

This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Data Availability

Article states: "The datasets used during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request. Further information about the dataset and conditions for access can be provided by contacting Dr. Ismail El Moudden at elmoudi@odu.edu. The data are held under the terms stipulated by the Virginia Health Information (VHI) database, which prohibits public sharing of the data to protect patient confidentiality and comply with legal restrictions."

Original Publication Citation

Moudden, I. E., Bittner, M. C., Karpov, M. V., Osunmakinde, I. O., Acheamponmaa, A., Nevels, B. J., Mbaye, M. T., Fields, T. L., Jordan, K., & Bahoura, M. (2025). Predicting mental health disparities using machine learning for African Americans in Southeastern Virginia. Scientific Reports, 15(1), 5900. https://doi.org/10.1038/s41598-025-89579-9

Download

Share

COinS