Predicting Perinatal Mortality Based on Maternal Health Status and Health Insurance Service using Homogeneous Ensemble Machine Learning Methods

Abstract

Background Perinatal mortality in Ethiopia is the highest in Africa, with 68 per 1000 pregnancies intrapartum deaths (death during the delivery). It is mainly associated with home delivery, which contributes for more than 75% of perinatal deaths. Financial constraints have a significant impact on timely access to maternal health (MH) care. Financial incentives, such as health insurance, can address the demand- and supply-side factors. This study, hence, aims to predict perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods Methods The data was collected from Ethiopian demographic health survey from 2011 to 2019 G.C. The data were pre-processed to get quality data that are suitable for a homogenous ensemble machine-learning algorithm to develop a model that predicts perinatal mortality. Results For constructing the proposed model, three experiments were conducted using random forest, gradient boosting, and cat boost algorithms. The overall accuracy of random forest, gradient boosting, and cat boost with 17 features is 89.95%, 90.24%, and 82%, respectively. Conclusions We finally concluded that perinatal mortality over time in Ethiopia is decreasing. We found out that perinatal mortality in Ethiopia is associated with risk factors such as community-based health insurance, mother's educational level, residence, mother age, wealth status, distance to the health facility, preterm, smoke cigarette, anemia level, haemoglobin level, and marital status.