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Machine learning models, particularly Ridge regression and Random Forest, improve depression prediction accuracy compared to logistic regression, with sleep disturbances and stress as key predictors.

This study compared logistic regression with ten machine learning models for predicting depression using WHO SAGE India wave 2 data. Ridge regression (AUC=0.716) and Random Forest (AUC=0.713) performed best, while most models had AUC below 0.70. Depression was more prevalent among younger adults, women, and those with poor self-rated health, stress, and sleep disturbances.

Last updated: Aug 29, 2026•0 RCTs•📖 Read as article →

Evidence Score

Evidence Score34/100
Human RCT☆☆☆☆☆
Meta-analysis☆☆☆☆☆
Mechanism★★★★★
Safety★★★★☆
Confidencelow

Study Evidence

Study 1. Depression prediction and key factors: A comparative analysis of logistic regression and machine learning models.

observational

Josten K, Jaganathan V · PloS one (2026)

Participants: N/A
Duration: Cross-sectional (wave 2 data)
Intervention: Comparison of logistic regression and ten machine learning algorithms (Random Forest, XGBoost, SVM, etc.) for depression prediction
Outcome: Depression prediction performance (accuracy, AUC, precision, recall, F1 score, etc.) and associated risk factors
Effect Size: N/A (AUC values reported: Ridge=0.716, Random Forest=0.713)
Population: Adults from WHO SAGE India wave 2 dataset

Result:

PubMed: 42658859DOI: 10.1371/journal.pone.0354668

Mechanism Graph

Data from WHO SAGE India wave 2 collected with depression as outcome
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Logistic regression identified significant predictors (age, feeling low/sad)
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Machine learning models trained on same predictors
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Feature importance analysis (Random Forest, XGBoost) identified age, health perception, quality of life, depressive symptoms as key
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Machine learning models achieved higher AUC than logistic regression

Limitations

  • ⚠Most models showed only moderate discriminative ability (AUC < 0.70)
  • ⚠Cross-sectional design limits causal inference
  • ⚠Specific sample from India may limit generalizability

Frequently Asked Questions

Which machine learning model performed best for depression prediction?▼

Ridge regression (AUC=0.716) and Random Forest (AUC=0.713) performed best, outperforming logistic regression.

What are the key factors associated with depression?▼

Younger age, female gender, poor self-rated health, stress, and sleep disturbances were associated with higher depression prevalence.

How does logistic regression compare to machine learning in depression prediction?▼

Logistic regression offers interpretability, while machine learning models generally provide higher predictive accuracy, but only slightly.

What was the study population?▼

The study used WHO SAGE India wave 2 data, which includes adults from India.

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References

  1. 1.Josten K, Jaganathan V. "Depression prediction and key factors: A comparative analysis of logistic regression and machine learning models.." PloS one, 2026. PMID: 42658859 DOI: 10.1371/journal.pone.0354668
Disclaimer: This content is for educational purposes only and is not medical advice. Evidence scores reflect the quality and quantity of available research, not clinical recommendations. Always consult a healthcare professional before starting any supplement or intervention.