Authors: Salifu Nanga, & Dr. Anani Lotsi
Department of Statistics, School of Graduate Studies, University of Ghana, & Sana Research and Consultancy Ghana Limited
Abstract
Child mortality is regarded as one of the most revealing measures of society’s ability to meet the needs of its people. The Millennium Development Goal 4 (MDG 4) advocates a reduction of under-five mortality rate by two-thirds between 1990 and 2015. The main objective of this study was to develop a validated set of statistical models and select the most appropriate model between logistic regression and K Nearest Neighbor to predict mortality among children under five and to compare the influence of selected risk factors on the probability of death before the age of 5 years among children in Ghana. The study revealed that the K Nearest Neighbor model was the most efficient in modeling Mortality in Children under five with a CCR of 83%. The Logistic Regression model will also do a good job at predicting mortality in children under five with a CCR of 81%. The highest educational level of mother, Age of mother at birth, Type of toilet facility used by family, alcohol consumption and the wealth index of family were discovered as the most important variables in predicting mortality amongst children under five in Ghana across both models.
KEYWORDS: Logistic Regression, Neural Networks, Children under five years
