NeoAI 1.0: Machine learning-based paradigm for prediction of neonatal and infant risk of death

The Neonatal mortality rate in the United States is 3.8 deaths per 1000 live births, which is comparably higher than other nations.

The aim of the proposed study is to design and develop Artificial Intelligence (AI) models (NeoAI 1.0, Global Biomedical Technologies, Inc., Roseville, CA, USA) on risk variables extracted from the National Center for Health Statistics (NCHS) data from 2014 to 2017 duration, consisting of birth-death infant files to predict neonatal and infant deaths.

The NCHS data consisted of 15.8 million live birth records, including 91,773 infant deaths, out of which 61,222 were neonatal (life <28 days) and the rest were non-deaths. We designed and developed two different kinds of systems, labelled as neonatal and infant death systems. The data preparation consisted of balancing the two classes using the Adaptive Synthetic oversampling technique (ADASYN) paradigm. The best features were extracted using mutual information followed by 5-fold cross-validation using four different models, namely AdaBoost, XGBoost, Random Forest, and Logistic Regression based on balanced and unbalanced paradigms.

XGBoost gave the best results for the neonatal system with AUC of 0.97 and 0.99 (p < 0.0001), while for the infant system, the scores were 0.91 and 0.99, both systems, without/with ADASYN integration, respectively. Further, there was a 60% increase in F1-score and sensitivity with ADASYN integration. The most important risk factors for classifier models along with feature extraction were maternal age and maternal race by Hispanic classification. Further, gestational age, labour aid and newborn condition were also part of the top five risk factors for these models.

NoeAI showed two independent powerful machine learning (ML) systems and selected the best risk predictors combined with classification models for neonatal and infant deaths. The response time of the online platform was less than a second.