Predicting mother and newborn skin-to-skin contact using a machine learning approach.

Abstract

Background: Despite the known benefits of skin-to-skin contact (SSC), limited data exists on its implementation, especially its influencing factors. The current study was designed to use machine learning (ML) to identify the predictors of SSC.

Methods: This study implemented predictive SSC approaches based on the data obtained from the "Iranian Maternal and Neonatal Network (IMaN Net)" from January 2020 to January 2022. A predictive model was built using nine statistical learning models (linear regression, logistic regression, decision tree classification, random forest classification, deep learning feedforward, extreme gradient boost model, light gradient boost model, support vector machine, and permutation feature classification with k-nearest neighbors). Demographic, obstetric, and maternal and neonatal clinical factors were considered as potential predicting factors and were extracted from the patient's medical records. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F_1 Score were measured to evaluate the diagnostic performance.

Results: Of 8031 eligible mothers, 3759 (46.8%) experienced SSC. The algorithms created by deep learning (AUROC: 0.81, accuracy: 0.75, precision: 0.67, recall: 0.77, and F_1 Score: 0.73) and linear regression (AUROC: 0.80, accuracy: 0.75, precision: 0.66, recall: 0.75, and F_1 Score: 0.71) had the highest performance in predicting SSC. Doula support, neonatal weight, gestational age, attending childbirth classes, and maternal age were the critical predictors for SSC based on the top two algorithms with superior performance.

Conclusions: Although this study found that the ML model performed well in predicting SSC, more research is needed to make a better conclusion about its performance.