Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms
Objective To identify risk factors for perimenopausal syndrome (PMS) among perimenopausal women using machine learning algorithms, and to construct a predictive model for the risk of developing moderate to severe PMS in perimenopausal women. Methods Perimenopausal women from 48 communites in Pudong...
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Editorial Office of New Medicine
2024-08-01
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| Series: | Yixue xinzhi zazhi |
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| Online Access: | https://yxxz.whuznhmedj.com/futureApi/storage/attach/2408/VkKxzxor1ZuT53BfGiqnmvp0XscH9CmKpOwhQWY2.pdf |
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| author | ZHANG Min GU Tingting GUAN Wei LIU Xiangxiang SHI Junyao |
| author_facet | ZHANG Min GU Tingting GUAN Wei LIU Xiangxiang SHI Junyao |
| author_sort | ZHANG Min |
| collection | DOAJ |
| description | Objective To identify risk factors for perimenopausal syndrome (PMS) among perimenopausal women using machine learning algorithms, and to construct a predictive model for the risk of developing moderate to severe PMS in perimenopausal women. Methods Perimenopausal women from 48 communites in Pudong New Area, Shanghai were selected as the study subjects. Based on the Kupperman index, participants were divided into the normal or mild PMS group and the moderate to severe PMS group. The data was randomly divided into training set and testing set, and feature selection was performed using the Boruta algorithm and SHAP algorithm. Logistic regression (LR), random forest (RF), support vector machine (SVM), and gradient boosting decision tree (GBDT) were constructed, and model performances were evaluated using accuracy, precision, recall, area under curve(AUC) of the receiver operating characteristic curve, and F1-score.Results A total of 856 perimenopausal women were included in the study, of which 557 were in the normal or mild PMS group and 299 were in the moderate to severe PMS group; 599 were in the training set and 257 were in the testing set. 9 features (employment status, exercise, age, menstrual condition, medical history, obesity, residence area, history of health education, household register) were selected as predictors for the final model using the Boruta algorithm and SHAP analysis. After parameter tuning, the 10-fold cross-validation AUC of LR, RF, SVM, and GBDT models based on the training set were 0.64, 0.77, 0.74, and 0.77, respectively. The AUC of the LR, RF, SVM, and GBDT models based on the testing set were 0.63, 0.69, 0.69, and 0.73, respectively, with recall rates of 0.59, 0.55, 0.55, and 0.62.Conclusion Among the constructed predictive models for the risk of developing moderate to severe PMS in perimenopausal women, the GBDT model demonstrated the best predictive performance and has potential clinical value. This study provides a new approach for the early identification and intervention of moderate to severe PMS in perimenopausal women. |
| format | Article |
| id | doaj-art-cd990429be5b474a9502c788f8e3e0ae |
| institution | OA Journals |
| issn | 1004-5511 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Editorial Office of New Medicine |
| record_format | Article |
| series | Yixue xinzhi zazhi |
| spelling | doaj-art-cd990429be5b474a9502c788f8e3e0ae2025-08-20T02:32:31ZzhoEditorial Office of New MedicineYixue xinzhi zazhi1004-55112024-08-0134887187910.12173/j.issn.1004-5511.2024051166520Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithmsZHANG MinGU TingtingGUAN WeiLIU XiangxiangSHI JunyaoObjective To identify risk factors for perimenopausal syndrome (PMS) among perimenopausal women using machine learning algorithms, and to construct a predictive model for the risk of developing moderate to severe PMS in perimenopausal women. Methods Perimenopausal women from 48 communites in Pudong New Area, Shanghai were selected as the study subjects. Based on the Kupperman index, participants were divided into the normal or mild PMS group and the moderate to severe PMS group. The data was randomly divided into training set and testing set, and feature selection was performed using the Boruta algorithm and SHAP algorithm. Logistic regression (LR), random forest (RF), support vector machine (SVM), and gradient boosting decision tree (GBDT) were constructed, and model performances were evaluated using accuracy, precision, recall, area under curve(AUC) of the receiver operating characteristic curve, and F1-score.Results A total of 856 perimenopausal women were included in the study, of which 557 were in the normal or mild PMS group and 299 were in the moderate to severe PMS group; 599 were in the training set and 257 were in the testing set. 9 features (employment status, exercise, age, menstrual condition, medical history, obesity, residence area, history of health education, household register) were selected as predictors for the final model using the Boruta algorithm and SHAP analysis. After parameter tuning, the 10-fold cross-validation AUC of LR, RF, SVM, and GBDT models based on the training set were 0.64, 0.77, 0.74, and 0.77, respectively. The AUC of the LR, RF, SVM, and GBDT models based on the testing set were 0.63, 0.69, 0.69, and 0.73, respectively, with recall rates of 0.59, 0.55, 0.55, and 0.62.Conclusion Among the constructed predictive models for the risk of developing moderate to severe PMS in perimenopausal women, the GBDT model demonstrated the best predictive performance and has potential clinical value. This study provides a new approach for the early identification and intervention of moderate to severe PMS in perimenopausal women.https://yxxz.whuznhmedj.com/futureApi/storage/attach/2408/VkKxzxor1ZuT53BfGiqnmvp0XscH9CmKpOwhQWY2.pdfperimenopausal syndromeperimenopausal womenmachine learningprediction |
| spellingShingle | ZHANG Min GU Tingting GUAN Wei LIU Xiangxiang SHI Junyao Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms Yixue xinzhi zazhi perimenopausal syndrome perimenopausal women machine learning prediction |
| title | Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms |
| title_full | Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms |
| title_fullStr | Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms |
| title_full_unstemmed | Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms |
| title_short | Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms |
| title_sort | construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms |
| topic | perimenopausal syndrome perimenopausal women machine learning prediction |
| url | https://yxxz.whuznhmedj.com/futureApi/storage/attach/2408/VkKxzxor1ZuT53BfGiqnmvp0XscH9CmKpOwhQWY2.pdf |
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