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.
This article is automatically generated from the structured evidence profile behind the claim above. Scores reflect the quality and quantity of available research, not clinical advice.
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. The current body of evidence comprises 1 study. EvidenceHub rates the overall confidence at 34/100 (low).
The Claim
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 conclusion is most relevant to: Adults from WHO SAGE India wave 2 dataset.
What the Research Shows
The conclusion draws on 1 linked study. Highlights from the cited literature:
- ▸Depression prediction and key factors: A comparative analysis of logistic regression and machine learning models. (PloS one, 2026) —
How It Works
The proposed biological pathway:
- ▸Data from WHO SAGE India wave 2 collected with depression as outcome
- ▸Logistic regression identified significant predictors (age, feeling low/sad)
- ▸Machine learning models trained on same predictors
- ▸Feature importance analysis (Random Forest, XGBoost) identified age, health perception, quality of life, depressive symptoms as key
- ▸Machine learning models achieved higher AUC than logistic regression
Who Might Benefit
Evidence fit by population:
- ▸Adults from WHO SAGE India wave 2 dataset
Recommended Dose
N/A
Limitations & Caveats
Important context when interpreting this evidence:
- ▸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.
References
- 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