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. 2021 Jul 16;11(7):1280. doi: 10.3390/diagnostics11071280

Predictors of Newborn’s Weight for Height: A Machine Learning Study Using Nationwide Multicenter Ultrasound Data

Ki Hoon Ahn 1,*,†, Kwang-Sig Lee 2,†, Se Jin Lee 3, Sung Ok Kwon 4, Sunghun Na 3,*, Kyongjin Kim 5, Hye Sim Kang 6, Kyung A Lee 7, Hye-Sung Won 8, Moon Young Kim 9, Han Sung Hwang 10, Mi Hye Park 7; Korean Society of Ultrasound in Obstetrics Gynecology Research Group
Editors: Antonio Farina, Paolo Ivo Cavoretto
PMCID: PMC8304217  PMID: 34359366

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.

Keywords: newborn, weight, height, estimated fetal weight, abdominal circumference

1. Introduction

Newborns’ underweight and children’s obesity are significant contributors for disease burden on the globe. One in every seven newborns in the world suffered from underweight in 2015 and these babies are more likely to experience death in the initial 28 days of life than common babies [1]. Similarly, 40 million children aged five or less in the world were characterized by overweight or obesity in 2016 [2], and this is likely to cause various diseases in their subsequent life such as asthma, cardiovascular disorders, depression, diabetes, dyslipidemia and hypertension [3,4,5,6,7,8]. In this context, the World Health Organization champions a global goal “No Increase in Childhood Overweight by 2025” [9].

Likewise, existing literature has attempted to examine newborn’s weight and its significant predictor variables among ultrasonographic data and maternal/delivery information [10,11,12,13]. These studies adopted linear regression, and hence they could not analyze (1) which predictor variables are more important for predicting newborn’s weight, or (2) what time is the best for taking ultrasonographic data. To overcome these limitations, a more recent study employed machine learning and made predictions for newborn’s body mass index from ultrasonographic data and maternal/delivery information [14]. The findings of this study agreed with those of existing literature stating that newborn’s weight/height2 would be a good alternative measure of newborn’s adiposity to newborn’s weight [15,16,17].

However, an optimal index for classifying underweight and overweight in children under 2 years of age has not been established yet, while conventional studies still ignore newborn’s weight/height and weight/height3 (Ponderal Index). Here, the Ponderal Index is designed to reflect the three-dimensional (volume) information (height3) [18]. To our best knowledge, 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. In this context, this study compared the performance measures for a variety of newborn’s weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. This study includes four weight-for-height indicators, that is, newborn’s weight, weight/height, weight/height2 and weight/height3. In addition, this study features 64 clinical, sonographic markers and 2949 mother–baby pairs. The ultimate goal of this study is to test the following null and alternative hypotheses:

Null Hypothesis: Newborn’s weight, newborn’s weight/height, newborn’s weight/height2 and newborn’s weight/height3 are equally suitable for ultrasonographic fetal biometry.

Alternative Hypothesis: Newborn’s weight, newborn’s weight/height, newborn’s weight/height2 and newborn’s weight/height3 are not equally suitable for ultrasonographic fetal biometry.

2. Materials and Methods

2.1. Participants

The source of data for this multi-center retrospective study was the same as in [14], the medical records of 2949 mother–baby pairs (see [14] for more detailed description). The study period was September 2019–March 2021 and the participating institutions were 48 general hospitals. This study was approved by institutional review boards of the forty-eight hospitals such as Korea University Anam Hospital (2019AN0433) participating in the study. Informed consent was waived by the institutional review boards. No administrative permissions or licenses were acquired by the authors to access the data used in this study. Then, data collection, analysis and interpretation followed.

2.2. Variables

The dependent variables were newborn’s weight, weight/height and weight/height3. Newborn’s weight and height were recorded at the time of birth. The following 64 independent variables were considered: (1) maternal data including age (years), children alive, height, pre-gestational weight, weight at delivery time, pre-gestational body mass index, body mass index at delivery time, term births, preterm births, abortions; (2) gestational age, ultrasound measures (see their notations in Table S1 (Supplementary Materials)); and (3) delivery/newborn data such as gestational age at delivery (weeks/days), Apgar scores in 1 and 5 min after delivery, caesarean delivery methods (no vs. yes), newborn’s sex—female (no vs. yes), neonatal intensive care unit hospitalization (no vs. yes). All participating institutions adopted Hadlock’s formula [19] for the estimation of EFW (except one participating institution that employed the Shinozuka’s formula [20]). These formulas use the same parameters and register similar performances to predict newborn’s weight [21].

2.3. Analysis

Five machine learning approaches were adopted for the prediction of newborn’s weight, weight/height and weight/height3: linear regression, random forest and artificial neural networks with one, two and three hidden layers [14,22]. Data on 2949 mother–baby pairs were split into training and validation sets with a 75:25 ratio (2212 vs. 737 mother–baby pairs). The mean squared error (MSE), the average of the squares of errors among 737 mother–baby pairs, was employed as a performance measure. The unit of the MSE is the squared unit of the dependent variable. The MSE is not appropriate for the comparison of model performance across different dependent variables with different units. For this reason, the MSE divided by the variance of the dependent variable (MSE over variance) was introduced for the comparison of model performance across different dependent variables with different units. Finally, random forest variable importance, the influence of a variable over average node impurity, was introduced for identifying most important predictor variables of newborn’s weight, weight/height and weight/height3 among ultrasonographic data and maternal/delivery information. R-Studio was used for the analysis on March 2021. It needs to be noted that the results for newborn’s weight/height2 were adopted from [14] and were compared with those for newborn’s weight, weight/height and weight/height3 in this study.

3. Results

Descriptive statistics in this study are given in Table 1. The respective median (Q2) values of newborn’s weight, weight/height, weight/height3, GA36AC1 (the first abdominal circumference in week 36 or later), GA36EFW1 (the first estimated fetal weight in week 36 or later) and gestational age at delivery time were 3.17 kg, 6.36 kg/m, 25.68 kg/m3, 322 mm, 2866 g and 38 weeks. The respective median values of GA21AC1 (the first abdominal circumference during week 21–week 35) and maternal body mass index at delivery time were 214.70 mm and 26.04 kg/m2. The proportion of neonatal intensive care unit hospitalization was 12% (354/2949). The MSEs of the five machine learning models for newborn’s weight, weight/height, weight/height2 and weight/height3 are presented in Table 2. The data were split, and the analysis was performed three times; then, the average MSE was obtained for each of the five statistical methods. Linear regression and the random forest were better models with smaller MSEs than the artificial neural networks for predicting newborn’s weight-for-height indicators. More importantly, newborn’s weight, newborn’s weight/height and newborn’s weight/height2 were better indicators with smaller MSE-over-variance measures than newborn’s weight/height3.

Table 1.

Descriptive statistics.

Continuous Variable SD Min Q1 Median Q3 Max
Newborn’s Weight (kg) 0.42 1.58 2.92 3.17 3.44 4.67
Newborn’s Weight/Height (kg/m) 0.73 3.66 5.92 6.36 6.82 9.16
Newborn’s Weight/Height3 (kg/m3) 3.06 14.64 23.90 25.68 27.60 37.04
Maternal Age 4.01 19.00 31.00 33.00 36.00 48.00
Maternal Height (cm) 5.17 140.00 158.00 161.00 165.00 181.00
Maternal Pregestational Weight (kg) 8.11 34.00 51.00 55.00 60.00 99.00
Maternal Weight at Delivery Time (kg) 8.60 45.00 62.85 68.00 74.00 92.80
Maternal Pregestational BMI 3.11 14.50 19.49 21.05 23.23 39.86
Maternal BMI at Delivery Time 3.11 16.33 24.21 26.04 28.23 40.00
GA11CRL1 (mm) 8.66 32.60 50.00 56.00 61.40 79.80
GA11NT1 (mm) 1.19 0.04 1.00 1.20 1.50 40.00
GA14BPD1 (mm) 3.68 23.10 32.40 34.70 36.40 67.00
GA14HC1 (mm) 10.29 72.60 123.70 126.40 128.70 200.00
GA14AC1 (mm) 11.58 34.00 101.40 107.30 112.00 219.00
GA14FL1 (mm) 2.96 9.10 18.00 19.80 21.30 32.50
GA14EFW1 (g) 31.88 14.00 137.00 152.00 165.00 345.00
GA20BPD1 (mm) 4.57 38.00 48.70 51.40 54.00 67.70
GA20HC1 (mm) 14.27 118.40 182.10 191.20 195.60 250.50
GA20AC1 (mm) 42.18 108.70 157.00 166.70 175.00 2113.30
GA20FL1 (mm) 3.67 25.90 32.80 35.00 37.30 45.50
GA20EFW1 (g) 110.31 109.00 367.00 425.00 481.00 980.00
GA21BPD1 (mm) 6.62 46.70 61.45 65.80 70.50 85.00
GA21HC1 (mm) 22.76 169.70 233.50 244.20 249.10 839.00
GA21AC1 (mm) 24.22 108.30 200.50 214.70 231.60 310.50
GA21FL1 (mm) 5.16 32.40 43.90 47.00 51.00 62.40
GA21EFW1 (g) 302.32 177.00 731.00 868.00 1098.00 2185.00
GA21BPD2 (mm) 5.57 61.00 73.60 77.40 80.60 92.90
GA21HC2 (mm) 15.30 193.20 276.90 283.00 284.60 386.20
GA21AC2 (mm) 23.64 155.00 244.00 258.40 272.10 369.70
GA21FL2 (mm) 4.58 43.40 53.40 56.40 59.20 68.60
GA21EFW2 (g) 393.44 644.00 1293.00 1508.00 1747.00 3569.00
GA21BPD3 (mm) 3.71 74.50 82.40 85.00 86.20 93.60
GA21HC3 (mm) 10.46 201.30 306.20 307.00 307.00 390.60
GA21AC3 (mm) 17.49 227.00 280.90 293.00 297.60 381.40
GA21FL3 (mm) 3.10 55.00 61.00 63.20 64.00 69.70
GA21EFW3 (g) 322.77 1211.00 1953.00 2186.00 2273.00 3661.00
GA36BPD1 (mm) 3.29 75.10 89.00 90.80 92.60 103.40
GA36HC1 (mm) 9.36 206.00 323.10 324.70 326.00 419.90
GA36AC1 (mm) 15.32 243.60 314.00 322.00 330.50 460.10
GA36FL1 (mm) 2.79 56.00 67.00 68.90 70.30 89.00
GA36EFW1 (g) 299.59 1577.00 2706.00 2866.00 3036.00 4172.00
Gestational Age—Delivery (Weeks) 1.53 20.00 38.00 38.00 39.00 42.00
Apgar Score in 1 Minute After Delivery 0.50 0.00 8.00 8.00 9.00 20.00
Apgar Score in 5 Minutes After Delivery 0.50 0.00 9.00 9.00 10.00 10.00
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Notes: SD: Standard Deviation; AC: Abdominal Circumference (mm); BPD: Biparietal Diameter (mm); CRL: Crown-Rump Length (mm); EFW: Estimated Fetal Weight (g); FL: Femur Length (mm); HC: Head Circumference (mm); NT: Nuchal Translucency (mm); GA11: Gestational Age, Week 11–Week 13; GA14: Gestational Age, Week 14–Week 19; GA20: Gestational Age, Week 20; GA21: Gestational Age, Week 21–Week 35; GA36: Gestational Age, Week 36 or Later.

Table 2.

Model performance: average mean squared error.

Dependent Variable/Model Run 1 Run 2 Run 3 MSE MSE/V †
Newborn’s Weight
Linear Regression 0.0579 0.1151 0.0584 0.0772 0.4376
Random Forest 0.0716 0.1236 0.0776 0.0909 0.5153
ANN 1 Layer * 0.2152 0.2866 0.3180 0.2733 1.5493
ANN 2 Layers 0.1748 0.5565 0.0858 0.2724 1.5442
ANN 3 Layers 0.2277 0.3174 0.2260 0.2571 1.4575
Newborn’s Weight/Height
Linear Regression 0.2593 0.5336 0.2656 0.3528 0.6620
Random Forest 0.2934 0.5583 0.3130 0.3882 0.7285
ANN 1 Layer 32.4925 32.9507 32.7599 32.7344 61.4269
ANN 2 Layers 32.4933 33.5946 33.0660 33.0513 62.0216
ANN 3 Layers 32.5195 33.5437 32.7257 32.9296 61.7932
Newborn’s Weight/Height3
Linear Regression 8.7276 23.9906 9.5329 14.0837 1.5041
Random Forest 8.9470 23.0724 9.3411 13.7868 1.4724
ANN 1 Layer 650.1868 668.5456 656.0115 658.2480 70.2986
ANN 2 Layers 652.3180 661.5827 648.1057 654.0021 69.8452
ANN 3 Layers 659.6003 656.9730 659.0836 658.5523 70.3311
Newborn’s Weight/Height2 [14]
Linear Regression 1.7933 1.9526 2.4774 2.0744 0.8747
Random Forest 1.8359 2.0782 2.5688 2.1610 0.9112
ANN 1 Layer 140.0307 158.5399 153.5595 150.7100 63.5478
ANN 2 Layers 140.0916 158.5026 165.5652 154.7198 65.2386
ANN 3 Layers 139.3295 158.6813 159.7421 152.5843 64.3381
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Note: * ANN Artificial Neural Network, † MSE/V Average Mean Squared Error/Variance.

Based on random forest variable importance, the top six predictor variables of newborn’s weight were the same with those of newborn’s weight/height and newborn’s weight/height2: Gestational age at delivery time, the first EFW and AC in week 36 or later, maternal weight and body mass index at delivery time, and the first BPD (biparietal diameter) in week 36 or later (See Table 3, Table 4, Table 5, Table S2(1–3) (Supplementary Materials) and Figure S1(1–3) (Supplementary Materials) in this study, Table 3 and Figure 1 in [14]). Eight among the top ten predictor variables of newborn’s weight/height3 were identical to those of newborn’s weight, weight/height and weight/height2. However, the importance ranking of the first EFW in week 36 or later was lower for newborn’s weight/height3 than for the other three indicators, and vice versa for the first AC during week 21–week 35. Indeed, the results of linear regression are informative regarding the effects of important predictor variables on newborn’s weight or weight/height. For example, newborn’s weight will increase by 170 g if gestational age at delivery time increases by 1 week. Newborn’s weight/height will increase by 0.05 g/m if the first EFW in week 36 or later increases by 1 g.

Table 3.

Random forest variable importance (VI) and regression coefficient from run 1: top 40 variables (dependent variable: newborn’s weight [g]).

Variable Random Forest Linear Regression
VI Value VI Rank Coefficient p-Value
Gestational Age—Delivery (Weeks) 81437955 1 * 170.2000 0.0000
GA36EFW1 (g) 59566065 2 * 0.3300 0.0000
GA36AC1 (mm) 40359557 3 * 3.3950 0.0055
Maternal Weight at Delivery Time (kg) 12600886 4 −2.9740 0.8606
GA36BPD1 (mm) 11536549 5 3.5670 0.2456
Maternal BMI at Delivery Time 9183122 6 25.9200 0.5537
Neonatal Intensive Care Unit Hospitalization 8666421 7 * −45.8000 0.0265
GA36FL1 (mm) 8167498 8 0.8415 0.8153
GA11CRL1 (mm) 7670171 9 −0.1804 0.8833
GA21AC1 (mm) 7394983 10 1.7720 0.0624
GA21BPD2 (mm) 7337688 11 3.0080 0.3310
Maternal Pregestational BMI 6918490 12 −12.7300 0.7878
GA21AC2 (mm) 6435064 13 1.2240 0.1581
GA21AC3 (mm) 6019105 14 * 3.7860 0.0039
GA36HC1 (mm) 5840288 15 −0.1518 0.8389
GA21BPD1 (mm) 5804584 16 3.5090 0.2108
GA20AC1 (mm) 5174051 17 0.2379 0.3931
GA20EFW1 (g) 5113174 18 −0.2566 0.1524
Maternal Pregestational Weight (kg) 5066332 19 1.8500 0.9193
GA21EFW1 (g) 4834716 20 * 0.2289 0.0472
GA21FL2 (mm) 4749409 21 −3.1040 0.4481
GA20HC1 (mm) 4713837 22 −0.0199 0.9811
GA21HC2 (mm) 4646094 23 −0.6821 0.3531
Apgar Score in 1 Minute After Delivery 4645375 24 0.4369 0.9574
Apgar Score in 5 Minutes After Delivery 4632233 25 2.7750 0.8193
GA21EFW3 (g) 4602151 26 0.0566 0.5850
Maternal Height (cm) 4570952 27 8.3310 0.3864
GA14BPD1 (mm) 4472051 28 * 6.8380 0.0270
Maternal Age 4345422 29 −2.3960 0.1433
GA20BPD1 (mm) 4297216 30 −6.3500 0.0544
GA21EFW2 (g) 4291562 31 * 0.2961 0.0005
GA21FL1 (mm) 4270426 32 −4.0100 0.2977
GA21HC1 (mm) 4261178 33 −0.3190 0.3783
GA20FL1 (mm) 4222331 34 * 9.9590 0.0214
GA14FL1 (mm) 4186594 35 * −9.4160 0.0486
GA11NT1 (mm) 4115745 36 −2.3880 0.6990
GA14AC1 (mm) 3751675 37 −0.6944 0.5587
GA14HC1 (mm) 3564844 38 0.9472 0.3295
GA21D2 3276785 39 * −15.1600 0.0002
GA36W1 3209062 40 * −90.3100 0.0000
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Notes: * p-Value < 0.05; AC: Abdominal Circumference (mm); BPD: Biparietal Diameter (mm); CRL: Crown-Rump Length (mm); EFW: Estimated Fetal Weight (g); FL: Femur Length (mm); HC: Head Circumference (mm); NT: Nuchal Translucency (mm); GA11: Gestational Age, Week 11–Week 13; GA14: Gestational Age, Week 14–Week 19; GA20: Gestational Age, Week 20; GA21: Gestational Age, Week 21–Week 35; GA36: Gestational Age, Week 36 or Later; W/D: Gestational Age–Weeks/Days.

Table 4.

Random forest variable importance (VI) and regression coefficient from run 1: top 40 variables (dependent variable: benn index: newborn’s weight/height).

Variable Random Forest Linear Regression
VI Value VI Rank Coefficient p-Value
Gestational Age—Delivery (Weeks) 213 1 * 0.2775 0.0000
GA36EFW1 (g) 162 2 * 0.0005 0.0026
GA36AC1 (mm) 138 3 * 0.0070 0.0050
Maternal Weight at Delivery Time (kg) 38 4 0.0093 0.7880
Maternal BMI at Delivery Time 37 5 0.0004 0.9967
GA36BPD1 (mm) 36 6 0.0072 0.2503
GA21AC1 (mm) 32 7 0.0033 0.0855
GA11CRL1 (mm) 32 8 −0.0002 0.9216
GA21BPD2 (mm) 31 9 0.0041 0.5133
GA21AC2 (mm) 27 10 0.0014 0.4243
GA36FL1 (mm) 23 11 0.0039 0.5915
Maternal Pregestational BMI 22 12 −0.0324 0.7362
GA21EFW2 (g) 22 13 * 0.0007 0.0001
Maternal Age 22 14 −0.0049 0.1416
GA21AC3 (mm) 21 15 * 0.0061 0.0218
GA21EFW1 (g) 20 16 * 0.0006 0.0166
GA21HC2 (mm) 20 17 −0.0010 0.5015
Neonatal Intensive Care Unit Hospitalization 20 18 −0.0460 0.2719
GA21BPD1 (mm) 19 19 0.0014 0.8032
GA36HC1 (mm) 18 20 −0.0016 0.2824
GA20AC1 (mm) 18 21 0.0007 0.2050
Maternal Pregestational Weight (kg) 18 22 0.0095 0.7981
GA14FL1 (mm) 18 23 −0.0176 0.0694
Apgar Score in 1 Minute After Delivery 17 24 0.0016 0.9248
GA21FL1 (mm) 17 25 * −0.0188 0.0166
GA21EFW3 (g) 17 26 0.0001 0.5429
GA21HC1 (mm) 17 27 −0.0008 0.2818
GA20EFW1 (g) 17 28 −0.0006 0.0912
Maternal Height (cm) 17 29 −0.0026 0.8923
GA21FL2 (mm) 16 30 −0.0125 0.1315
GA20BPD1 (mm) 16 31 −0.0076 0.2596
GA14BPD1 (mm) 16 32 * 0.0145 0.0214
GA20HC1 (mm) 15 33 −0.0004 0.8220
GA11W1 14 34 0.0061 0.8621
GA20FL1 (mm) 14 35 0.0134 0.1265
GA11NT1 (mm) 13 36 −0.0048 0.7014
Apgar Score in 5 Minutes After Delivery 13 37 0.0130 0.5985
GA14AC1 (mm) 13 38 −0.0015 0.5294
GA14HC1 (mm) 12 39 0.0002 0.9016
GA14EFW1 (g) 12 40 0.0004 0.7231
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Notes: * p-Value < 0.05; AC: Abdominal Circumference (mm); BPD: Biparietal Diameter (mm); CRL: Crown-Rump Length (mm); EFW: Estimated Fetal Weight (g); FL: Femur Length (mm); HC: Head Circumference (mm); NT: Nuchal Translucency (mm); GA11: Gestational Age, Week 11–Week 13; GA14: Gestational Age, Week 14–Week 19; GA20: Gestational Age, Week 20; GA21: Gestational Age, Week 21–Week 35; GA36: Gestational Age, Week 36 or Later; W/D: Gestational Age–Weeks/Days.

Table 5.

Random forest variable importance (VI) and regression coefficient from run 1: top 40 variables (dependent variable: ponderal index: newborn’s weight/height3).

Variable Random Forest Linear Regression
VI Value VI Rank Coefficient p-Value
GA21AC1 (mm) 1804 1 0.0061 0.6314
GA36AC1 (mm) 1598 2 0.0296 0.0697
Gestational Age—Delivery (Weeks) 1417 3 * 0.3250 0.0000
GA21BPD2 (mm) 1230 4 0.0145 0.7260
Maternal BMI at Delivery Time 1205 5 −0.1966 0.7364
GA36EFW1 (g) 1196 6 0.0006 0.5464
GA21AC2 (mm) 1068 7 −0.0012 0.9168
Maternal Age 1054 8 −0.0311 0.1542
GA11CRL1 (mm) 868 9 0.0031 0.8499
GA21EFW2 (g) 808 10 * 0.0031 0.0064
Maternal Pregestational BMI 728 11 −0.2214 0.7256
Apgar Score in 5 Minutes After Delivery 727 12 −0.1616 0.3187
GA21EFW1 (g) 719 13 * 0.0032 0.0373
GA36BPD1 (mm) 672 14 0.0163 0.6903
GA21HC2 (mm) 671 15 −0.0012 0.9037
Maternal Weight at Delivery Time (kg) 662 16 0.0748 0.7408
GA21FL2 (mm) 657 17 * −0.1148 0.0356
GA20FL1 (mm) 572 18 −0.0284 0.6232
GA20BPD1 (mm) 559 19 0.0174 0.6932
GA20EFW1 (g) 558 20 −0.0032 0.1774
Maternal Height (cm) 545 21 −0.1067 0.4059
GA36FL1 (mm) 541 22 0.0587 0.2220
GA11NT1 (mm) 530 23 −0.0205 0.8034
GA21BPD1 (mm) 527 24 −0.0365 0.3298
GA14BPD1 (mm) 520 25 0.0536 0.1934
GA21FL1 (mm) 512 26 * −0.1944 0.0002
GA20AC1 (mm) 489 27 0.0057 0.1269
GA20HC1 (mm) 472 28 −0.0023 0.8358
Maternal Pregestational Weight (kg) 462 29 0.0990 0.6846
GA21AC3 (mm) 462 30 0.0126 0.4697
GA14HC1 (mm) 444 31 −0.0049 0.7033
GA14FL1 (mm) 437 32 −0.0740 0.2455
Apgar Score in 1 Minute After Delivery 431 33 0.0915 0.4015
GA11W1 426 34 −0.0138 0.9522
GA21FL3 (mm) 407 35 0.0151 0.8092
GA21HC1 (mm) 398 36 −0.0063 0.1907
GA21BPD3 (mm) 375 37 −0.0044 0.9290
GA21EFW3 (g) 374 38 0.0005 0.7374
GA14AC1 (mm) 370 39 −0.0016 0.9180
GA36HC1 (mm) 355 40 −0.0150 0.1332
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Notes: * p-Value < 0.05; AC: Abdominal Circumference (mm); BPD: Biparietal Diameter (mm); CRL: Crown-Rump Length (mm); EFW: Estimated Fetal Weight (g); FL: Femur Length (mm); HC: Head Circumference (mm); NT: Nuchal Translucency (mm); GA11: Gestational Age, Week 11–Week 13; GA14: Gestational Age, Week 14–Week 19; GA20: Gestational Age, Week 20; GA21: Gestational Age, Week 21–Week 35; GA36: Gestational Age, Week 36 or Later; W/D: Gestational Age–Weeks/Days.

Figure 1.

Figure 1

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Random forest variable importance values of top 20 predictors for newborn’s large-for-gestational-age.

Finally, the random forest variable importance of predictors for large-for-gestational-age (LGA) and small-for-gestational-age (SGA) are presented in Figure 1 and Figure 2, respectively. The top six predictor variables of newborn’s weight, weight/height and weight/height2 also ranked within the top seven for LGA and the top eight for SGA: gestational age at delivery time, the first EFW and AC in week 36 or later, maternal weight and body mass index at delivery time, and the first BPD in week 36 or later. Moreover, the importance rankings of the top three predictors for newborn’s weight, weight/height and weight/height2 were within the top four for LGA and the top three for SGA as well: gestational age at delivery time, and the first EFW and AC in week 36 or later.

Figure 2.

Figure 2

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Random forest variable importance values of top 20 predictors for newborn’s small-for-gestational-age.

The results of this study support the alternative hypothesis: newborn’s weight, newborn’s weight/height, newborn’s weight/height2 and newborn’s weight/height3 are not equally suitable for ultrasonographic fetal biometry. It was found in this study that newborn’s weight, newborn’s weight/height and newborn’s weight/height2 are more suitable for ultrasonographic fetal biometry than newborn’s weight/height3.

4. Discussion

4.1. Principal Findings

Newborn’s weight/height and newborn’s weight/height2 are more suitable for ultrasonographic fetal biometry with smaller MSE-over-variance measures than newborn’s weight/height3. The top six predictor variables of newborn weight were the same as those of newborn weight/height and those of newborn weight/height2: gestational age at delivery time, the first EFW and AC in week 36 or later, maternal weight and body mass index at delivery time, and the first BPD 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.

4.2. Clinical and Research Implications

The findings of this study above are consistent with those of the previous study [14]: week 36 or later is the best time to take ultrasonographic data, and AC and EFW are the most important predictor variables of newborn’s weight/height2 together with gestational age at delivery and maternal body mass index at delivery. However, the previous study ignored newborn’s weight/height, which can be another good alternative measure of newborn’s adiposity to newborn’s weight. As a matter of fact, there is no consensus on the best weight-for-height indicator for newborns and children under the age of 2, in part because babies born earlier are more heterogeneous in terms of weight for height than babies born later [18]. Newborn thinness is considered to be a risk factor for adult chronic disease, but it is not clear which of a newborn’s weight-for-height indicators (e.g., weight/height, weight/height2, weight/height3) are the best indicators for adult chronic disease [18]. Given that newborn thinness is known to be a risk factor for adult chronic disease, it would be worthwhile to shift our attention to newborn weight-for-height indicators and their prenatal predictors. This will help to develop a new research tradition covering health conditions across different life periods, i.e., prenatal, newborns, children and adults. In this context, this study compared the performance measures for a variety of newborn weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. To the best of our knowledge, there has been no study on this topic in this direction. The findings of this study suggest that machine learning with ultrasonographic data would be an effective noninvasive approach for predicting a newborn’s weight, weight/height and weight/height2. Specifically, the results of this study bring the following clinical implication for the prognosis of adiposity for newborns and children under the age of 2 (with no current consensus on their best weight-for-height indicators): clinicians are recommended to use a newborn’s weight, weight/height or weight/height2 as an indicator of a newborn’s adiposity when they employ ultrasonographic fetal biometry.

4.3. Strengths and Limitations

To the best of our knowledge, 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. In this context, this study compared the performance measures for a variety of newborn’s weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. This study included four weight-for-height indicators, that is, newborn’s weight, weight/height, weight/height2 and weight/height3. In addition, this study featured 64 clinical, sonographic markers and 2949 mother–baby pairs. However, this study had some limitations. Firstly, this study did not include possible mediating effects. Secondly, this study did not consider socioeconomic determinants, disease information (diabetes, gastroesophageal reflux disease, hypertension, periodontitis), medication history (benzodiazepine, calcium channel blocker, nitrate, progesterone, proton pump inhibitor, sleeping pills, antidepressant) and obstetric information (in vitro fertilization, myoma uteri, prior cone). These factors have been reported to influence delivery outcome [23,24,25] and it would be a useful extension to consider these new variables. Thirdly, additional examination of symptomatic vs. asymptomatic, single vs. multiple gestation, is expected to provide more insights and implications on this important topic.

5. Conclusions

This is the first study to compare the performance measures for a variety of newborn’s weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. Newborn’s weight, newborn’s weight/height and newborn’s weight/height2 are more suitable for ultrasonographic fetal biometry with smaller MSE-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.

Acknowledgments

We appreciate the following researchers and hospitals participated in this study: Korea University Anam Hospital (A.K.H., K.H.I., K.H.Y., and L.K.S.), Kangwon National University Hospital (N.S.H., L.S.J., and K.S.O.), Konkuk University Hospital (H.H.S.), Ewha Womans University Hospital (P.M.H.), Catholic University of Korea Seoul St. Mary’s Hospital (K.H.S.), Catholic University of Korea Eunpyeong St. Mary’s Hospital (K.J.Y.), CHA Gangnam Medical Center (K.M.Y.), Kyung Hee University Hospital at Gangdong (S.H.J.), Hallym University Kangdong Sacred Heart Hospital (M.J.S.), Gangneung Asan Hospital (J.D.H.), Kangbuk Samsung Hospital (S.J.H.), Konyang University Hospital (K.T.Y.), Kyungpook National University Hospital (S.W.J.), Gyeongsang National University Hospital (P.J.K.), Keimyung University Dongsan Medical Center (B.J.G.), Korea University Guro Hospital (C.G.J.), Hanyang University Guri Hospital (B.H.Y.), National Health Insurance Service Ilsan Hospital (K.E.H.), Gachon University Gil Hospital (K.S.Y.), Dankook University Hospital (K.Y.D.), Daegu Catholic University Medical Center (H.S.Y.), Hallym University Dongtan Sacred Heart Hospital (K.K.S.), Pusan National University Hospital (K.S.C.), Inje University Busan Paik Hospital (K.Y.N.), Catholic University of Korea Bucheon St. Mary’s Hospital (S.K.J. and S.J.E.), CHA Bundang Medical Center (L.J.Y.), Sungkyunkwan University Samsung Medical Center (O.S.Y.), Inje University Sanggye Paik Hospital (S.Y.S.), Seoul National University Hospital (L.S.M.), Seoul National University Boramae Medical Center (K.B.J.), Soon Chun Hyang University Hospital (C.G.Y.), Ulsan University Asan Medical Center (W.H.S. and L.M.Y.), Yonsei Univeristy Sinchon Severance Hospital (K.Y.H.), Pusan National University Yangsan Hospital (L.D.H.), Ulsan University Hospital (L.S.J.), Inha University Hospital (C.S.R.), Dongguk University Ilsan hospital (P.H.S.), Inje University Ilsan Paik Hospital (K.H.S.), Chonnam National University Hospital (K.Y.H. and K.J.W.), Jeonbuk National University Hospital (J.Y.J. and L.D.H.), Jeonju Presbyterian Medical Center (K.K.J.), Jeju National University Hospital (K.H.S.), Chosun University Hospital (C.S.J. and C.J.H.), Chung-Ang University Hospital (K.G.J.), Soon Chun Hyang University Cheonan Hospital (K.Y.S.), Chungnam National University Hospital (L.M.A.), Hallym University Kangnam Sacred Heart Hospital (S.J.E.), Hanyang University Hospital (H.J.K.).

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/diagnostics11071280/s1, Table S1: Notations of Gestational Age and Ultrasound Measures, Table S2: Random Forest Variable Importance (VI) and Regression Coefficient from Run 1: All Variables (S2-1 Newborn’s Weight, S2-2 Newborn’s Weight/Height, S2-3 Newborn’s Weight/Height3), Figure S1: Random Forest Variable Importance Values of Top 20 Predictors (S1-1 Newborn’s Weight, S1-2 Newborn’s Weight/Height, S1-3 Newborn’s Weight/Height3).

Click here for additional data file. (340.4KB, zip)

Author Contributions

K.H.A., K.-S.L. and S.N. contributed to conception, design, data analysis, manuscript writing and manuscript review. All authors contributed to conception, design and manuscript review. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was approved by institutional review boards of forty-eight hospitals such as Korea University Anam Hospital (2019AN0433; approval date: 20 December 2020) participating in the study. Informed consent was waived by the institutional review boards. No administrative permissions or licenses were acquired by the authors to access the data used in this study.

Informed Consent Statement

Informed consent was waived by the institutional review boards, given that data were deidentified.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (340.4KB, zip)

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.

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