Investigate Risk Factors and Predict Neonatal and Infant Mortality Based on Maternal Determinants using Homogenous Ensemble Methods

Abstract

Ethiopia, one of the Sub-Saharan countries, has been affected by preventable and treatable causes of childhood mortality. According to the Ethiopia Mini Demographic and Health Survey (EMDHS) 2019, the child mortality rate, which measures under-five child deaths per one thousand children, was 43 during the 5 years preceding the survey. This study, hence, aims to investigate risk factors and predict neonatal and infant mortality based on maternal data. To this end, data was collected from the Ethiopia Demographic and Health Surveys (EDHS) and several experiments were conducted using homogenous ensemble methods to develop a model that best identifies risk factors and predicts neonatal and infant mortality in Ethiopia. A decision tree with bagging and AdaBoost achieved an accuracy of 94.34% and 94.79% and area under ROC of 86% and 87% respectively. Naïve Bayes achieved 87.60% and 89.5% with bagging and AdaBoost. A decision tree with AdaBoost ensemble method performed better with 97.19% and 99.92% F-measure and recall, respectively. A maximum increase of 4 % accuracy for weak classifiers was achieved with the ensemble classification. As the finding suggest the interventions towards neonatal and infant mortality may need to take the factors related to maternal determinants into account. The application of heterogeneous ensemble methods is similar challenges may enhance the performance of the prediction model.