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Predictors of Newborn’s Weight for Height: A Machine Learning Study Using Nationwide Multicenter Ultrasound Data
- Source :
- Diagnostics, Volume 11, Issue 7, Diagnostics, Vol 11, Iss 1280, p 1280 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- There has been no machine learning study with a rich collection of clinical, sonographic markers to compare the performance measures for a variety of newborns’ weight-for-height indicators. This study compared the performance measures for a variety of newborns’ weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. The source of data for this study was a multi-center retrospective study with 2949 mother–newborn pairs. The mean-squared-error-over-variance measures of five machine learning approaches were compared for newborn’s weight, newborn’s weight/height, newborn’s weight/height2 and newborn’s weight/hieght3. Random forest variable importance, the influence of a variable over average node impurity, was used to identify major predictors of these newborns’ weight-for-height indicators among ultrasonographic data and maternal/delivery information. Regarding ultrasonographic fetal biometry, newborn’s weight, newborn’s weight/height and newborn’s weight/height2 were better indicators with smaller mean-squared-error-over-variance measures than newborn’s weight/height3. Based on random forest variable importance, the top six predictors of newborn’s weight were the same as those of newborn’s weight/height and those of newborn’s weight/height2: gestational age at delivery time, the first estimated fetal weight and abdominal circumference in week 36 or later, maternal weight and body mass index at delivery time, and the first biparietal diameter in week 36 or later. These six predictors also ranked within the top seven for large-for-gestational-age and the top eight for small-for-gestational-age. In conclusion, newborn’s weight, newborn’s weight/height and newborn’s weight/height2 are more suitable for ultrasonographic fetal biometry with smaller mean-squared-error-over-variance measures than newborn’s weight/height3. Machine learning with ultrasonographic data would be an effective noninvasive approach for predicting newborn’s weight, weight/height and weight/height2.
- Subjects :
- Medicine (General)
Clinical Biochemistry
Machine learning
computer.software_genre
Article
03 medical and health sciences
0302 clinical medicine
R5-920
newborn
030225 pediatrics
Medicine
030212 general & internal medicine
abdominal circumference
business.industry
Ultrasound
Abdominal circumference
Gestational age
Retrospective cohort study
weight
Fetal weight
Fetal biometry
estimated fetal weight
Artificial intelligence
business
computer
Body mass index
Weight for height
height
Subjects
Details
- Language :
- English
- ISSN :
- 20754418
- Database :
- OpenAIRE
- Journal :
- Diagnostics
- Accession number :
- edsair.doi.dedup.....9e5b6caa0f6906bcf4ca3e8acd5c4c5a
- Full Text :
- https://doi.org/10.3390/diagnostics11071280