Non-cognitive factors influencing early stroke symptom recognition among Korean working-class males with hypertension and diabetes: an integrated multi-output gradient boosting and logistic regression approach
Background: This study aims to investigate the non-cognitive factors influencing the recognition of early stroke symptoms among Korean working class males with diabetes using an integrated machine learning approach combining Multi-Output Gradient Boosting and logistic regression models. Methods: Dat...
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| Format: | Article |
| Language: | English |
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MRE Press
2025-06-01
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| Series: | Journal of Men's Health |
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| Online Access: | https://oss.jomh.org/files/article/20250627-565/pdf/JOMH2024061601.pdf |
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| _version_ | 1849414767295332352 |
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| author | Haewon Byeon |
| author_facet | Haewon Byeon |
| author_sort | Haewon Byeon |
| collection | DOAJ |
| description | Background: This study aims to investigate the non-cognitive factors influencing the
recognition of early stroke symptoms among Korean working class males with diabetes
using an integrated machine learning approach combining Multi-Output Gradient
Boosting and logistic regression models. Methods: Data from the Korea National
Health and Nutrition Examination Survey (KNHANES) from 2016 to 2022 were utilized,
including 4125 working class males with diabetes. Participants were divided into two
groups based on their recognition of early stroke symptoms. The integrated machine
learning model was trained on 80% of the dataset and tested on the remaining 20%. Key
predictors were identified, and logistic regression analysis provided odds ratios (OR)
and 95% confidence intervals (CI) for significant factors. Results: The study found that
72% of participants recognized early stroke symptoms, while 28% did not. Significant
predictors of non-recognition included younger age (β = −0.05, OR = 0.95, p < 0.01),
higher Body Mass Index (BMI) (β = 0.12, OR = 1.13, p < 0.01), hypertension (β =
0.28, OR = 1.32, p < 0.01), elevated cholesterol (β = 0.03, OR = 1.03, p < 0.01)
and triglycerides (β = 0.04, OR = 1.04, p < 0.01), depression (β = 0.25, OR = 1.28,
p < 0.01), stress (β = 0.18, OR = 1.20, p < 0.01), smoking (β = 0.10, OR = 1.11, p
< 0.01) and alcohol consumption (β = 0.08, OR = 1.08, p < 0.01). Positive factors
included regular physical activity (β = −0.20, OR = 0.82, p < 0.01) and participation
in diabetes education programs (β = −0.15, OR = 0.86, p < 0.01). Conclusions: The
findings highlight the multifactorial nature of stroke symptom recognition and suggest
that targeted interventions focusing on both physiological and psychological factors,
as well as promoting healthy lifestyle behaviors, can significantly improve symptom
recognition and health outcomes in working class males with diabetes. |
| format | Article |
| id | doaj-art-0b0c42d8eeea4e5d9dfcb8b1d3b33542 |
| institution | Kabale University |
| issn | 1875-6867 1875-6859 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MRE Press |
| record_format | Article |
| series | Journal of Men's Health |
| spelling | doaj-art-0b0c42d8eeea4e5d9dfcb8b1d3b335422025-08-20T03:33:43ZengMRE PressJournal of Men's Health1875-68671875-68592025-06-01216213210.22514/jomh.2025.077S1875-6867(25)00394-XNon-cognitive factors influencing early stroke symptom recognition among Korean working-class males with hypertension and diabetes: an integrated multi-output gradient boosting and logistic regression approachHaewon Byeon0Workcare Digital Health Lab, Department of Employment Service Policy, Korea University of Technology and Education, 31253 Cheonan, Republic of KoreaBackground: This study aims to investigate the non-cognitive factors influencing the recognition of early stroke symptoms among Korean working class males with diabetes using an integrated machine learning approach combining Multi-Output Gradient Boosting and logistic regression models. Methods: Data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2016 to 2022 were utilized, including 4125 working class males with diabetes. Participants were divided into two groups based on their recognition of early stroke symptoms. The integrated machine learning model was trained on 80% of the dataset and tested on the remaining 20%. Key predictors were identified, and logistic regression analysis provided odds ratios (OR) and 95% confidence intervals (CI) for significant factors. Results: The study found that 72% of participants recognized early stroke symptoms, while 28% did not. Significant predictors of non-recognition included younger age (β = −0.05, OR = 0.95, p < 0.01), higher Body Mass Index (BMI) (β = 0.12, OR = 1.13, p < 0.01), hypertension (β = 0.28, OR = 1.32, p < 0.01), elevated cholesterol (β = 0.03, OR = 1.03, p < 0.01) and triglycerides (β = 0.04, OR = 1.04, p < 0.01), depression (β = 0.25, OR = 1.28, p < 0.01), stress (β = 0.18, OR = 1.20, p < 0.01), smoking (β = 0.10, OR = 1.11, p < 0.01) and alcohol consumption (β = 0.08, OR = 1.08, p < 0.01). Positive factors included regular physical activity (β = −0.20, OR = 0.82, p < 0.01) and participation in diabetes education programs (β = −0.15, OR = 0.86, p < 0.01). Conclusions: The findings highlight the multifactorial nature of stroke symptom recognition and suggest that targeted interventions focusing on both physiological and psychological factors, as well as promoting healthy lifestyle behaviors, can significantly improve symptom recognition and health outcomes in working class males with diabetes.https://oss.jomh.org/files/article/20250627-565/pdf/JOMH2024061601.pdfearly stroke symptom recognitiondiabetic workersnon-cognitive factorsmachine learningmulti-output gradient boosting |
| spellingShingle | Haewon Byeon Non-cognitive factors influencing early stroke symptom recognition among Korean working-class males with hypertension and diabetes: an integrated multi-output gradient boosting and logistic regression approach Journal of Men's Health early stroke symptom recognition diabetic workers non-cognitive factors machine learning multi-output gradient boosting |
| title | Non-cognitive factors influencing early stroke symptom recognition among Korean working-class males with hypertension and diabetes: an integrated multi-output gradient boosting and logistic regression approach |
| title_full | Non-cognitive factors influencing early stroke symptom recognition among Korean working-class males with hypertension and diabetes: an integrated multi-output gradient boosting and logistic regression approach |
| title_fullStr | Non-cognitive factors influencing early stroke symptom recognition among Korean working-class males with hypertension and diabetes: an integrated multi-output gradient boosting and logistic regression approach |
| title_full_unstemmed | Non-cognitive factors influencing early stroke symptom recognition among Korean working-class males with hypertension and diabetes: an integrated multi-output gradient boosting and logistic regression approach |
| title_short | Non-cognitive factors influencing early stroke symptom recognition among Korean working-class males with hypertension and diabetes: an integrated multi-output gradient boosting and logistic regression approach |
| title_sort | non cognitive factors influencing early stroke symptom recognition among korean working class males with hypertension and diabetes an integrated multi output gradient boosting and logistic regression approach |
| topic | early stroke symptom recognition diabetic workers non-cognitive factors machine learning multi-output gradient boosting |
| url | https://oss.jomh.org/files/article/20250627-565/pdf/JOMH2024061601.pdf |
| work_keys_str_mv | AT haewonbyeon noncognitivefactorsinfluencingearlystrokesymptomrecognitionamongkoreanworkingclassmaleswithhypertensionanddiabetesanintegratedmultioutputgradientboostingandlogisticregressionapproach |