A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulse
Background and objective Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary sy...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-02-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076231160323 |
| _version_ | 1797866799832760320 |
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| author | Weiying Wang Weiwei Zeng Shunli He Yulin Shi Xinmin Chen Liping Tu Bingyi Yang Jiatuo Xu Xiuqi Yin |
| author_facet | Weiying Wang Weiwei Zeng Shunli He Yulin Shi Xinmin Chen Liping Tu Bingyi Yang Jiatuo Xu Xiuqi Yin |
| author_sort | Weiying Wang |
| collection | DOAJ |
| description | Background and objective Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques. Methods A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting classifier, and support vector machine) were used to construct the classification models to predict the presence of polycystic ovary syndrome. Results TB-L, TB-a, TB-b, TC-L, TC-a, h 3 , and h 4 /h 1 in tongue and pulse parameters were statistically associated with polycystic ovary syndrome presence. Among the several machine learning techniques, the support vector machine model was optimal for the comprehensive evaluation of this dataset and deduced the area under the receiver operating characteristic curve, DeLong test, calibration curve, and decision curve analysis. Conclusion The machine learning model with tongue and pulse factors can predict the existence of polycystic ovary syndrome precisely. |
| first_indexed | 2024-04-09T23:31:08Z |
| format | Article |
| id | doaj.art-bb8b2b944b2948199bf3487fdabb28d3 |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-04-09T23:31:08Z |
| publishDate | 2023-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-bb8b2b944b2948199bf3487fdabb28d32023-03-21T05:33:19ZengSAGE PublishingDigital Health2055-20762023-02-01910.1177/20552076231160323A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulseWeiying Wang0Weiwei Zeng1Shunli He2Yulin Shi3Xinmin Chen4Liping Tu5Bingyi Yang6Jiatuo Xu7Xiuqi Yin8 Department of Gynecology and Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine, Shanghai, P.R. China Department of Gynecology and Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine, Shanghai, P.R. China Department of Gynecology and Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine, Shanghai, P.R. China Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, P.R. China Department of Gynecology and Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine, Shanghai, P.R. China Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, P.R. China Department of Gynecology and Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine, Shanghai, P.R. China Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, P.R. China Department of Gynecology and Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine, Shanghai, P.R. ChinaBackground and objective Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques. Methods A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting classifier, and support vector machine) were used to construct the classification models to predict the presence of polycystic ovary syndrome. Results TB-L, TB-a, TB-b, TC-L, TC-a, h 3 , and h 4 /h 1 in tongue and pulse parameters were statistically associated with polycystic ovary syndrome presence. Among the several machine learning techniques, the support vector machine model was optimal for the comprehensive evaluation of this dataset and deduced the area under the receiver operating characteristic curve, DeLong test, calibration curve, and decision curve analysis. Conclusion The machine learning model with tongue and pulse factors can predict the existence of polycystic ovary syndrome precisely.https://doi.org/10.1177/20552076231160323 |
| spellingShingle | Weiying Wang Weiwei Zeng Shunli He Yulin Shi Xinmin Chen Liping Tu Bingyi Yang Jiatuo Xu Xiuqi Yin A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulse Digital Health |
| title | A new model for predicting the occurrence
of polycystic ovary syndrome: Based on data
of tongue and pulse |
| title_full | A new model for predicting the occurrence
of polycystic ovary syndrome: Based on data
of tongue and pulse |
| title_fullStr | A new model for predicting the occurrence
of polycystic ovary syndrome: Based on data
of tongue and pulse |
| title_full_unstemmed | A new model for predicting the occurrence
of polycystic ovary syndrome: Based on data
of tongue and pulse |
| title_short | A new model for predicting the occurrence
of polycystic ovary syndrome: Based on data
of tongue and pulse |
| title_sort | new model for predicting the occurrence of polycystic ovary syndrome based on data of tongue and pulse |
| url | https://doi.org/10.1177/20552076231160323 |
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