Ensemble Learning for Stroke Classification Based on Generative Adversarial Networks.
Background and Objective: Class imbalance in stroke datasets often results in increased misdiagnosis rates, biased risk factor analysis, and limited clinical utility of machine learning models. This study proposed a method combining Generative Adversarial Networks (GAN) with a hard voting ensemble t...
| Publicado en: | Health & Technology Vol. 16; no. 3; pp. 527 - 538 |
|---|---|
| Autores principales: | , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193278021&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193278021 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21907188 BEWM jtl: Health & Technology issn: 21907188 maglogo: N pubinfo: dt: May2026 vid: 16 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 193278021 192536276 193278021 193278021 10.1007/s12553-026-01062-1 193278021 ppf: 527 ppct: 11 formats: tig: atl: Ensemble Learning for Stroke Classification Based on Generative Adversarial Networks. aug: au: Hong, Xiaorui Wang, Ping Bai, Jingchen Zhu, Suling affil: https://ror.org/01mkqqe32 School of Public Health, Lanzhou University, 730000, Lanzhou, Gansu, China sug: subj: Stroke Risk Factors Risk Assessment Stroke Classification Ensemble Learning Generative Adversarial Networks Prediction Models Prediction Algorithms Classification Algorithms Sensitivity and Specificity Evaluation Human Questionnaires Logistic Regression Comparative Studies Random Forest Boosting Machine Learning Algorithms Odds Ratio Age Factors Depression Income Poverty Fatty Acids, Unsaturated Food Intake Adult Middle Age Aged Aged, 80 and Over Self Report Stroke Patients Descriptive Statistics Data Analysis Software Nonexperimental Studies Validation Studies United States Male Female Psychological Tests Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background and Objective: Class imbalance in stroke datasets often results in increased misdiagnosis rates, biased risk factor analysis, and limited clinical utility of machine learning models. This study proposed a method combining Generative Adversarial Networks (GAN) with a hard voting ensemble to enhance the accuracy of stroke risk identification, thereby supporting improved stroke prevention and control. Methods: Data were from 4,238 participants in the NHANES database (2007–2018). A balanced subset resampling strategy combined with LASSO-logistic regression was employed to identify stroke risk factors. GAN was used to generate minority class samples to achieve dataset balance, and its performance was compared with traditional data balancing methods. Four base classifiers were constructed using selected features, and predictions were integrated via hard voting, including Logistic Regression (LR), Random Forest (RF), XGBoost, and Gradient Boosting Decision Tree (GBDT). Performance was assessed using sensitivity, F1 score, and G-mean. Results: Age (OR = 1.050, P < 0.001) and depression score (DPQ-9, OR = 1.065, P < 0.001) significantly increased stroke risk. Family income-to-poverty ratio (PIR, OR = 0.911, P = 0.026) and total polyunsaturated fatty acid intake (PUFA, OR = 0.988, P = 0.046) exhibited protective effects. GAN-based data balancing improved XGBoost's sensitivity from 0.018 to 0.915. The voting ensemble achieved an F1 score of 0.956 and specificity of 1.000. SHapley Additive exPlanations (SHAP) analysis confirmed the dominant role of these factors. Conclusion: This study integrated GAN-based data generation with a hard voting mechanism to effectively address class imbalance in stroke prediction. It significantly improved model stability and clinical identification capability, providing a reliable tool to screen high-risk populations and formulate targeted prevention strategies. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|