多囊卵巢
接收机工作特性
人工智能
支持向量机
特征选择
舌头
机器学习
计算机科学
生物
医学
内分泌学
病理
糖尿病
胰岛素抵抗
作者
Weiying Wang,Weiwei Zeng,Shunli He,Yulin Shi,Xinmin Chen,Liping Tu,Bingyi Yang,Jiatuo Xu,Xiuqi Yin
出处
期刊:Digital health
[SAGE]
日期:2023-01-01
卷期号:9: 205520762311603-205520762311603
被引量:2
标识
DOI:10.1177/20552076231160323
摘要
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.
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