Predictive Analysis of common risk factors in Neonates using Machine Learning

Abstract

To identify the clinical–demographic risk factors contributing to birth asphyxia ongoing pregnancies and to suggest preventive measures. The proposed methodology is used to identify the risk factor associated with birth asphyxia such as- maternal risk factors, type of deliveries, educational status of mothers, the age distribution of mothers, gestation period, birth weight, etc. A hospital-based prospective observational study was carried out on 220 participants. After obtaining the consent of either of the parents, the detailed maternal data and history were taken in a structured questionnaire designed for the study purpose. The statistical evaluation of model performance is evaluated using these standard metrics.In this research, three types of machine learning classification algorithms which are Two class Neural networks, Two-Class Boosted Decision Tree, Multi-Class Decision Jungle were used to build the Birth asphyxia model to identify the overall risk factors associated with birth asphyxia. The findings showed that the Two-Class Boosted Decision Tree achieved the highest accuracy at 94.5%