ArticleScientific reports2024
Detecting depression severity using weighted random forest and oxidative stress biomarkers.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Just a Minute: A Pupil-Based Machine Learning Approach to Depression Screening.Applied psychophysiology and biofeedback · 2026Article
- Oxidative Stress in Treatment-Resistant and Refractory Depression: A Hidden Therapeutic Target?Molecular neurobiology · 2026Review
- Beyond classification metrics: a psychometric-aware benchmark for data augmentation in imbalanced student mental health surveys.Frontiers in digital health · 2026Article
- An inflammatory biomarker panel for prediabetes classification using interpretable machine learning.PloS one · 2026Article
- What machine learning teaches us about depression prediction across the life course: An exploratory comparison of predictive models.SSM - population health · 2025Article
- Hierarchical random forest model, inflammation and oxidative stress as predictors of the atherogenic index of plasma and diabetes progression.Scientific reports · 2025Article
- Diagnostics of diabetic retinopathy based on fundus photos using machine learning methods with advanced feature engineering algorithms.Scientific reports · 2025Article
- Antioxidant Properties of Food-Derived Lactic Acid Bacteria: A Review.Microbial biotechnology · 2025Review
- Neurobiological Mechanisms of Electroconvulsive Therapy: Molecular Perspectives of Brain Stimulation.International journal of molecular sciences · 2025Review
- Dual level dengue diagnosis using lightweight multilayer perceptron with XAI in fog computing environment and rule based inference.Scientific reports · 2025Article
- Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification.Life (Basel, Switzerland) · 2025Article
- Prediction of depressive disorder using machine learning approaches: findings from the NHANES.BMC medical informatics and decision making · 2025Article
- Depression Recognition Using Daily Wearable-Derived Physiological Data.Sensors (Basel, Switzerland) · 2025Article
- Isolation, identification, and evaluation of the antioxidant properties of lactic acid bacteria strains isolated from meat environment.PloS one · 2025Article
- Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- Antioxidants in neuropsychiatric disorder prevention: neuroprotection, synaptic regulation, microglia modulation, and neurotrophic effects.Frontiers in neuroscience · 2024Review
- Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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