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The effect of community intervention based on deep learning-established early screening system and model construction
Xiaowei Chen1, Meijun Zhang2, Liping Ni2
1Dongyang Traditional Chinese Medicine Hospital, Dongyang, Zhejiang, 322100, China. vnfr913@163.com.
BMC Neurology
|July 15, 2026
Summary
Machine learning identifies stroke risk factors in the community. Individualized interventions significantly reduced stroke incidence compared to routine care, demonstrating a novel approach to stroke prevention.
Area of Science:
- Cardiovascular disease research
- Machine learning applications in healthcare
- Public health interventions
Background:
- Stroke represents a significant cardiovascular complication.
- Machine learning (ML) offers a novel approach to stroke risk assessment.
- Community-based strategies are crucial for stroke prevention.
Purpose of the Study:
- To establish a community-based stroke risk identification system using ML.
- To evaluate the effectiveness of individualized interventions in preventing stroke.
- To identify key risk factors for stroke within a community setting.
Main Methods:
- Retrospective analysis of stroke patients and healthy individuals.
- Utilized Random Forest and LASSO regression for feature selection.
- Employed multivariate logistic regression for stroke risk prediction model.
- Conducted a prospective study with randomized groups receiving tailored vs. routine interventions.
Main Results:
- Identified triglycerides, apolipoprotein B, serum creatinine, hsCRP, and homocysteine as stroke risk factors.
- The intervention group showed a significantly lower stroke incidence (5.41%) compared to the control group (28.6%).
- Time to stroke occurrence was significantly longer in the intervention group.
Conclusions:
- ML-driven community-based risk identification effectively screens high-risk populations.
- Individualized interventions demonstrably reduce stroke incidence.
- This approach holds promise for proactive stroke prevention strategies.