Application of machine learning to identify risk factors of birth asphyxia

Abstract

Background Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk of birth asphyxia is necessary. The present study used a machine learning model to predict birth asphyxia. Methods Women who gave birth at a tertiary Hospital in Bandar Abbas, Iran, were retrospectively evaluated from January 2020 to January 2022. Data were extracted from the Iranian Maternal and Neonatal Network, a valid national system, by trained recorders using electronic medical records. Demographic factors, obstetric factors, and prenatal factors were obtained from patient records. Machine learning was used to identify the risk factors of birth asphyxia. Eight machine learning models were used in the study. To evaluate the diagnostic performance of each model, six metrics, including area under the receiver operating characteristic curve, accuracy, precision, sensitivity, specificity, and F1 score were measured in the test set. Results Of 8888 deliveries, we identified 380 women with a recorded birth asphyxia, giving a frequency of 4.3%. Random Forest Classification was found to be the best model to predict birth asphyxia with an accuracy of 0.99. The analysis of the importance of the variables showed that maternal chronic hypertension, maternal anemia, diabetes, drug addiction, gestational age, newborn weight, newborn sex, preeclampsia, placenta abruption, parity, intrauterine growth retardation, meconium amniotic fluid, mal-presentation, and delivery method were considered to be the weighted factors. Conclusion Birth asphyxia can be predicted using a machine learning model. Random Forest Classification was found to be an accurate algorithm to predict birth asphyxia. More research should be done to analyze appropriate variables and prepare big data to determine the best model.

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

“…It was tested and validated by Google AI using a large training dataset of 128,175 and two separate publicly available datasets (Gulshan et al, 2016 , 2019 ). ML models are also being trained to identify newborns with risk of birth asphyxia (Onu et al, 2017 ; Sachin et al, 2017 ; Darsareh et al, 2023 ). Given the high neonatal mortality in low and middle income countries, this could prove to be a revolutionary application of AI in such circumstances.…”

The determinants of neonatal asphyxia in the tropical province of China: A case-control study

“…[12,13] Various international studies found that multiple risk factors: socio-demographic (mother's age, residence, mother's marital status, educational status, occupation), antepartum (antenatal care [ANC] follow up, parity, history of prior neonatal death, pre-eclampsia), intrapartum (prolonged labor, status of amniotic fluid, cephalopelvic disproportion), neonatal (preterm babies, birth weight, gestational age) are associated with NA. [14][15][16][17][18] However, information related to determinants of neonata asphyxia in Hainan referral hospitals of China, is limited. Therefore, the aim of this study was to identify women at risk of NA by combining multiple factors, minimize the rate of NA, and lighten the burden of family and society by improving maternal and neonatal management.…”