Identifying First-Trimester Risk Factors for SGA-LGA Using Weighted Inheritance Voting Ensemble Learning

Abstract

The classification of fetuses as Small for Gestational Age (SGA) and Large for Gestational Age (LGA) is a critical aspect of neonatal health assessment. SGA and LGA, terms used to describe fetal weights that fall below or above the expected weights for Appropriate for Gestational Age (AGA) fetuses, indicate intrauterine growth restriction and excessive fetal growth, respectively. Early prediction and assessment of latent risk factors associated with these classifications can facilitate timely medical interventions, thereby optimizing the health outcomes for both the infant and the mother. This study aims to leverage first-trimester data to achieve these objectives. This study analyzed data from 7943 pregnant women, including 424 SGA, 928 LGA, and 6591 AGA cases, collected from 2015 to 2021 at the Third Affiliated Hospital of Sun Yat-sen University in Guangzhou, China. We propose a novel algorithm, named the Weighted Inheritance Voting Ensemble Learning Algorithm (WIVELA), to predict the classification of fetuses into SGA, LGA, and AGA categories based on biochemical parameters, maternal factors, and morbidity during pregnancy. Additionally, we proposed algorithms for relevance determination based on the classifier to ascertain the importance of features associated with SGA and LGA. The proposed classification solution demonstrated a notable average accuracy rate of 92.12% on 10-fold cross-validation over 100 loops, outperforming five state-of-the-art machine learning algorithms. Furthermore, we identified significant latent maternal risk factors directly associated with SGA and LGA conditions, such as weight change during the first trimester, prepregnancy weight, height, age, and obstetric factors like fetal growth restriction and birthing LGA baby. This study also underscored the importance of biomarker features at the end of the first trimester, including HDL, TG, OGTT-1h, OGTT-0h, OGTT-2h, TC, FPG, and LDL, which reflect the status of SGA or LGA fetuses. This study presents innovative solutions for classifying and identifying relevant attributes, offering valuable tools for medical teams in the clinical monitoring of fetuses predisposed to SGA and LGA conditions during the initial stage of pregnancy. These proposed solutions facilitate early intervention in nutritional care and prenatal healthcare, thereby contributing to enhanced strategies for managing the health and well-being of both the fetus and the expectant mother.

Keywords: AGA (appropriate for gestational age); LGA (large for gestational age); SGA (small for gestational age); first trimester ending; machine learning.

Figure 1

Our methodology for classifying SGA-LGA data.

Figure 2

Illustration of the disadvantages of automatic feature selection approaches in ML. (a) Feature importance list generated by Random Forest classifier. (b) Run Algorithm 1 with Random Forest classifier to find the best feature group.

Figure 3

Our proposed classifier for SGA-LGA data.

Figure 4

Comparison of algorithms using the ManualFeature group, evaluated by mean accuracy across 100 iterations of 10-fold cross-validation.

Figure 5

Comparison of algorithms using the ManualFeature group, evaluated by negative MSE across 100 iterations of 10-fold cross-validation.

Figure 6

Find the best feature group based on Algorithm 1.

Figure 7

Feature importance list generated by our proposed classifier on ManualFeature group.

Figure 8

Feature importance generated by Algorithm 4 on BestFeature group.

Figure 9

Beeswarm plot of feature importance based on SHAP values using BestFeature group.