A logistic regression-based risk classification model can predict insomnia symptoms in community residents with satisfactory performance.
A machine learning model using logistic regression achieved AUC values of 0.812 in internal validation and 0.795 in external validation for classifying insomnia symptom risk. The top five contributing factors were sleep time, sleep latency, bedtime, health status, and age.
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.
A logistic regression-based risk classification model can predict insomnia symptoms in community residents with satisfactory performance. The current body of evidence comprises 1 study. EvidenceHub rates the overall confidence at 34/100 (low).
The Claim
A logistic regression-based risk classification model can predict insomnia symptoms in community residents with satisfactory performance.
This conclusion is most relevant to: Community-based residents of Hebei Province, China (16,848 eligible participants from 30 counties).
What the Research Shows
The conclusion draws on 1 linked study. Highlights from the cited literature:
- ▸Machine learning-based insomnia symptom risk classification model for residents of Hebei province. (Public health, 2026) —
How It Works
The proposed biological pathway:
- ▸Collect cross-sectional survey data on sleep-related variables and insomnia symptoms (ISI)
- ▸Train six machine learning algorithms on 2021 dataset (training set n=6863)
- ▸Select logistic regression as optimal model based on AUC performance
- ▸Apply SHAP analysis to identify top influential features: sleep time, sleep latency, bedtime, health status, age
Who Might Benefit
Evidence fit by population:
- ▸Community-based residents of Hebei Province, China (16,848 eligible participants from 30 counties)
Recommended Dose
N/A
Limitations & Caveats
Important context when interpreting this evidence:
- ▸Cross-sectional design prevents causal inference
- ▸Model was developed and validated only within Hebei Province, limiting generalizability to other populations
Frequently Asked Questions
What was the best performing machine learning model in this study?▼
The logistic regression model showed the best classification performance with AUC values of 0.812 (internal) and 0.795 (external).
Which factors were most important for predicting insomnia symptoms?▼
The top five factors were sleep time, sleep latency, bedtime, health status, and age, as identified by SHAP analysis.
How many participants were included in this study?▼
The study enrolled 16,848 eligible participants from 30 counties in Hebei Province, China.
What tool was used to assess insomnia symptoms?▼
Insomnia symptoms were assessed using the Insomnia Severity Index (ISI).
References
- 1.Wang Y, Qi C, Cao X, Meng F, Zhao Y, Bai P, Song J, Wang S, Liu B, Song B. “Machine learning-based insomnia symptom risk classification model for residents of Hebei province..” Public health, 2026. PMID: 42413427 DOI: 10.1016/j.puhe.2026.106409