ARTIFICIAL NEURAL NETWORKS IN FORECASTING THE QUARTERLY MALARIA CASES IN NIGERIA

Authors

  • Bukar, A.B. Dept. of Mathematics, Nigerian Defence Academy, Kaduna
  • Dikko, H.G. Dept. of Statistics, Ahmadu Bello University, Zaria

Keywords:

Artificial Neural Network, Training Data, Nodes, World Health Organization, Malaria

Abstract

The objective of the study is to train artificial neural networks (AMORE SOFTWARE) with a suitable data obtained from the WHO record collected from the National Surveillance System (NSS). The architecture of the neural networks in use has two inputs nodes ????????−1and ????????−4 with one hidden layer and one output layer????????. They were used to forecast quarterly malaria cases in Nigeria. The data used were from 1990 to 2003. A total of 32 data series was used in the training and ends up with R2 = 0.9946 and 24 used in the validation of the neural networks with R2 = 0.5759. The forecasting accuracy was found to be R2 = 0.9665 , which validate the networks.

Downloads

Download data is not yet available.

Downloads

Published

2022-04-27

Issue

Vol. 11 No. 1 (2017): Academy Journal of Science and Engineering (AJSE)

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This is an open-access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. 

The Authors own the copyright of the articles.