Detecting depression severity using weighted random forest and oxidative stress biomarkers

This study employs machine learning to detect the severity of major depressive disorder (MDD) through binary and multiclass classifications. We compared models that used only biomarkers of oxidative stress with those that incorporate sociodemographic and health-related factors. Data collected from 8...

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Bibliographic Details
Published in:Scientific reports 2024-07, Vol.14 (1), p.16328-18, Article 16328
Main Authors: Bader, Mariam, Abdelwanis, Moustafa, Maalouf, Maher, Jelinek, Herbert F.
Format: Article
Language:English
Subjects:
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Adult
Area Under Curve
Biochemistry
Biomarkers
Classification
Decision making
Depression - diagnosis
Depression severity
Depressive Disorder, Major - diagnosis
Female
Humanities and Social Sciences
Humans
Machine Learning
Male
Mental depression
Middle Aged
multidisciplinary
Oxidative Stress
PHQ-9
Random Forest
Science
Science (multidisciplinary)
Severity of Illness Index
Statistical analysis
Statistical models
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Description
Summary:This study employs machine learning to detect the severity of major depressive disorder (MDD) through binary and multiclass classifications. We compared models that used only biomarkers of oxidative stress with those that incorporate sociodemographic and health-related factors. Data collected from 830 participants, based on the Patient Health Questionnaire (PHQ-9) score, inform our analysis. In binary classification, the Random Forest (RF) classifier achieved the highest Area Under the Curve (AUC) of 0.84 when all features were included. In multiclass classification, the AUC improved from 0.84 with only oxidative stress biomarkers to 0.88 when all characteristics were included. To address data imbalance, weighted classifiers, and Synthetic Minority Over-sampling Technique (SMOTE) approaches were applied. Weighted random forest (WRF) improved multiclass classification, achieving an AUC of 0.91. Statistical tests, including the Friedman test and the Conover post-hoc test, confirmed significant differences between model performances, with WRF using all features outperforming others. Feature importance analysis shows that oxidative stress biomarkers, particularly GSH, are top ranked among all features. Clinicians can leverage the results of this study to improve their decision-making processes by incorporating oxidative stress biomarkers in addition to the standard criteria for depression diagnosis.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-67251-y