IMR Press / CEOG / Volume 46 / Issue 6 / DOI: 10.12891/ceog5022.2019

Clinical and Experimental Obstetrics & Gynecology (CEOG) is published by IMR Press from Volume 47 Issue 1 (2020). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on as a courtesy and upon agreement with S.O.G.

Open Access Original Research
Artificial neural network models for prediction of premature ovarian failure
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1 Huarun Wuhan Iron and Steel General Hospital of Wuhan University of Science and Technology, Wuhan, China
2 School of Nursing, Wuchang University of Technology, Wuhan, China
*Correspondence: (YAN WU)
Clin. Exp. Obstet. Gynecol. 2019, 46(6), 958–963;
Published: 10 December 2019

Aim: The aim of this study is to develop and optimize artificial neural network models for accurate prediction of premature ovarian failure (POF), to test these models on data collected prospectively from different centres. Materials and Methods: The study used data from 316 women presenting to six communities governed by a street in Wuhan, Hubei, China. Unbiased randomization was divided into training samples (177 cases), test samples (44 cases), and adherence samples (95 cases). Data from training samples and test samples were used to train the models, which were then tested on independent data from adherence samples. From 35 potential factors, variables were selected by Analytic Hierarchy Process (AHP), and then were used in the ANN model to make the prediction. Results: The predicting accuracy of the train set, validation set, and test set were 98.73%, 94.15%, and 92.15%, respectively, when the generalization ability was verified. Conclusion: This study confirms that artificial neural network can offer a useful approach for developing diagnostic algorithms for POF prediction.

Premature ovarian failure
Analytic hierarchy process
Artificial neural network
Prediction model
Figure 1.
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