摘要
Objectives: A novel analysis was undertaken to select a significant ultrasonographic parameter (USP) for classifying fetuses to support artificial neural network (ANN), and thus to enhance the accuracy of fetal weight estimation. Methods: In total, 2127 singletons were examined by prenatal ultrasound within 3 days before delivery. First, correlation analysis was used to determine a significant USP for fetal grouping. Second, K-means algorithm was utilized for fetal size classification based on the selected USP. Finally, stepwise regression analysis was used to examine input parameters of the ANN model. Results: The estimated fetal weight (EFW) of the new model showed mean absolute percent error (MAPE) of 5.26 ± 4.14% and mean absolute error (MAE) of 157.91 ± 119.90 g. Comparison of EFW accuracy showed that the new model significantly outperformed the commonly-used EFW formulas (all p < 0.05). Conclusion: We proved the importance of choosing a specific grouping parameter for ANN to improve EFW accuracy.
原文 | English |
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頁(從 - 到) | 545-553 |
頁數 | 9 |
期刊 | Taiwanese Journal of Obstetrics and Gynecology |
卷 | 51 |
發行號 | 4 |
DOIs | |
出版狀態 | Published - 2012 12月 |
All Science Journal Classification (ASJC) codes
- 婦產科