Key result
Machine learning models, specifically LSTM and Voting classifiers, identified polycystic ovary syndrome based on radial pulse wave parameters with a testing accuracy of 72.17% and an F1 score of 0.818.
Why the study?
Patients with polycystic ovary syndrome experience endocrine disorders that may present vascular function changes, but machine learning classification using radial pulse wave parameters had not been evaluated.
Does machine learning classification based on radial pulse wave analysis accurately identify patients with polycystic ovary syndrome compared to healthy controls?
Case-Control (n=459)
Yes
Does machine learning classification based on radial pulse wave analysis accurately identify patients with polycystic ovary syndrome compared to healthy controls?
Machine learning models utilizing radial pulse wave parameters can identify patients with polycystic ovary syndrome with acceptable accuracy, providing an objective basis for traditional Chinese medicine pulse diagnosis.
Answers are grounded in this paper and cite their sources.
ML-based pulse wave analysis may aid PCOS detection; leaves open clinical utility pending prospective validation.
BACKGROUND: Patients with Polycystic ovary syndrome (PCOS) experienced endocrine disorders that may present vascular function changes. This study aimed to classify and predict PCOS by radial pulse wave parameters using machine learning (ML) methods and to provide evidence for objectifying pulse diagnosis in traditional Chinese medicine (TCM). METHODS: A case-control study with 459 subjects divided into a PCOS group and a healthy (non-PCOS) group. The pulse wave parameters were measured and analyzed between the two groups. Seven supervised ML classification models were applied, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forest, Logistic Regression, Voting, and Long Short Term Memory networks (LSTM). Parameters that were significantly different were selected as input features and stratified k-fold cross-validations training was applied to the models. RESULTS: There were 316 subjects in the PCOS group and 143 subjects in the healthy group. Compared to the healthy group, the pulse wave parameters h3/h1 and w/t from both left and right sides were increased while h4, t4, t, As, h4/h1 from both sides and right t1 were decreased in the PCOS group (P < 0.01). Among the ML models evaluated, both the Voting and LSTM with ensemble learning capabilities, demonstrated competitive performance. These models achieved the highest results across all evaluation metrics. Specifically, they both attained a testing accuracy of 72.174% and an F1 score of 0.818, their respective AUC values were 0.715 for the Voting and 0.722 for the LSTM. CONCLUSION: Radial pulse wave signal could identify most PCOS patients accurately (with a good F1 score) and is valuable for early detection and monitoring of PCOS with acceptable overall accuracy. This technique can stimulate the development of individualized PCOS risk assessment using mobile detection technology, furthermore, gives physicians an intuitive understanding of the objective pulse diagnosis of TCM. TRIAL REGISTRATION: Not applicable.
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Lim et al. (2023) conducted a case-control in Polycystic ovary syndrome (PCOS) (n=459). Radial pulse wave parameters vs. Healthy controls was evaluated on Testing accuracy of LSTM model for PCOS classification (AUC 0.722). Machine learning models, specifically LSTM and Voting classifiers, identified polycystic ovary syndrome based on radial pulse wave parameters with a testing accuracy of 72.17% and an F1 score of 0.818.
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