Random Forest Regression Model For Estimation of Neonatal Levels In Nigeria

International Journal of Computer Science and Engineering

© 2020 by SSRG - IJCSE Journal

Volume 7 Issue 7

Year of Publication : 2020

Authors : C. Managwu, D.Matthias, N. Nwaibu

10.14445/23488387/IJCSE-V7I7P107

How to Cite?

C. Managwu, D.Matthias, N. Nwaibu, "Random Forest Regression Model For Estimation of Neonatal Levels In Nigeria," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 7, pp. 41-44, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I7P107

Abstract:

Health is considered to be a fundamental necessity of all living beings, especially humans, hence its availability is a critical component of the Human Development Index (HDI) measurement of human health. Neonatal (NMR) and infant (IMR) mortality rates are not topics that require too much time or energy for most of us on our day-to-day thinking, but they are also vital to guaranteeing our standard of life. Nigeria's attempts to minimize under-five mortality have been skewed against neglect of neonates in favour of childhood mortality, and as such the literature lacks adequate knowledge
to estimate neonatal rates accurately. Awareness of the rate of neonatal deaths as well as neonatal mortality determinants is important for the design of intervention programs to improve neonatal survival. Hence, this research was conducted to apply Random Forest Regression in predicting neonatal death rates to help better prepare healthcare systems for future occurrences.
This model will be educated using the existing dataset of actual neonatal death rates from 1970 through to 2018.

Keywords:

Neonate, Neonatal Mortality, Big Data, Human Activity Pattern.

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