Construction of prediction model of neonatal necrotizing enterocolitis based on machine learning algorithms / 中国新生儿科杂志

ABSTRACT

Objective:To construct prediction models of necrotizing enterocolitis (NEC) using machine learning (ML) methods.Methods:From January 2015 to October 2021, neonates with suspected NEC symptoms receiving abdominal ultrasound examinations in our hospital were retrospectively analyzed. The neonates were assigned into NEC group (modified Bell's staging≥Ⅱ) and non-NEC group for diagnostic prediction analysis (dataset 1). The NEC group was subgrouped into surgical NEC group (staging≥Ⅲ) and conservative NEC group for severity analysis (dataset 2). Feature selection algorithms including extremely randomized trees, elastic net and recursive feature elimination were used to screen all variables. The diagnostic and severity prediction models for NEC were established using logistic regression, support vector machine (SVM), random forest, light gradient boosting machine and other ML methods. The performances of different models were evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, negative predictive value and positive predictive value.Results:A total of 536 neonates were enrolled, including 234 in the NEC group and 302 in the non-NEC group (dataset 1).70 were in the surgical NEC group and 164 in the conservative NEC group (dataset 2). The variables selected by extremely randomized trees showed the best predictive performance in two datasets. For diagnostic prediction models, the SVM model had the best predictive performance, with AUC of 0.932 (95% CI 0.891-0.973) and accuracy of 0.844 (95% CI 0.793-0.895). A total of 11 predictive variables were determined, including portal venous gas, intestinal dilation, neutrophil percentage and absolute monocyte count at the onset of illness. For NEC severity prediction models, the SVM model showed the best predictive performance, with AUC of 0.835 (95% CI 0.737-0.933) and accuracy of 0.787 (95% CI 0.703-0.871). A total of 25 predictive variables were identified, including age of onset, C-reactive protein and absolute neutrophil count at clincial onset. Conclusions:NEC prediction model established using feature selection algorithm and SVM classification model in ML is helpful for the diagnosis of NEC and grading of disease severity.