Predictors of Newborn's Weight for Height: A Machine Learning Study Using Nationwide Multicenter Ultrasound Data
- 주제(키워드) newborn , weight , height , estimated fetal weight , abdominal circumference
- 주제(기타) Medicine, General & Internal
- 설명문(일반) [Ahn, Ki Hoon] Korea Univ, Anam Hosp, Dept Obstet & Gynecol, Coll Med, Seoul 02841, South Korea; [Lee, Kwang-Sig] Korea Univ, Coll Med, AI Ctr, Anam Hosp, Seoul 02841, South Korea; [Lee, Se Jin; Na, Sunghun] Kangwon Natl Univ, Dept Obstet & Gynecol, Kangwon Natl Univ Hosp, Sch Med, Kangwon, Chuncheon, South Korea; [Kwon, Sung Ok] Kangwon Natl Univ, Dept Prevent Med, Sch Med, Kangwon 24289, Chuncheon, South Korea; [Kim, Kyongjin] Presbyterian Med Ctr, Dept Obstet & Gynecol, Jeonju 54987, South Korea; [Kang, Hye Sim] Jeju Natl Univ, Dept Obstet & Gynecol, Jeju 63241, South Korea; [Lee, Kyung A.; Park, Mi Hye] Ewha Womans Univ, Ewha Med Ctr, Ewha Med Inst, Dept Obstet & Gynecol,Coll Med, Seoul 07804, South Korea; [Won, Hye-Sung] Univ Ulsan, Asan Med Ctr, Dept Obstet & Gynecol, Coll Med, Seoul 05505, South Korea; [Kim, Moon Young] CHA Univ, CHA Gangnam Med Ctr, Dept Obstet & Gynecol, Seoul 06135, South Korea; [Hwang, Han Sung] Konkuk Univ, Res Inst Med Sci, Dept Obstet & Gynecol, Sch Med, Seoul 05030, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 MDPI
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000182317
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/diagnostics11071280
초록/요약
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/height(2) and newborn's weight/hieght(3). 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/height(2) were better indicators with smaller mean-squared-error-over-variance measures than newborn's weight/height(3). 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/height(2): 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/height(2) are more suitable for ultrasonographic fetal biometry with smaller mean-squared-error-over-variance measures than newborn's weight/height(3). Machine learning with ultrasonographic data would be an effective noninvasive approach for predicting newborn's weight, weight/height and weight/height(2).
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