Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

2Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia

Sudhir Kumar Mohapatra^1*, Srinivas Prasad², Getachew Mekuria Habtemariam³ and Mohammed Siddique⁴

¹Faculty of Emerging Technologies, Sri Sri University, Cuttack, India

²GITAM University, Vishakapatnam, India

³Addis Ababa Science and Technology University, Addis Ababa, Ethiopia

⁴Department of Mathematics, Centurion University of Technology and Management, Odisha, India

Abstract

The Ethiopian government doing for the past two decades for attaining millennium development goals agenda for preventing childhood mortality by improving the child health’s to change the country image to the rest of the world in reduction of childhood mortality. This study contributes some values in the improvement of childhood health by analyzing the determinants infant and child mortality by using machine learning techniques. Different reports indicate that the distribution of childhood mortality differs in the world. According to the United Nations Inter-Agency Group, 2017 noted the nationwide young child fatality ratio dropped into 56 percentages starting from 1990 to 2016. In 1990 the deaths per 1,000 live births were 93 percent as well as in 2016 counted 41 percent. Ethiopian demographic and health survey 2016 report of 2017 indicates that for the previous 5 years review, the young child fatality ratio was 67 deaths per 1,000 live births, beside this, the neonate mortality ratio was 48 deaths ...