IMR Press / FBL / Volume 25 / Issue 2 / DOI: 10.2741/4808

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on imrpress.com as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Article
A new backpropagation neural network classification model for prediction of incidence of malaria
Show Less
1 Department of Computer Science and Engineering, NIT Goa, India
2 Advanced Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USA
3 AtheroPoint™, Roseville, CA, USA
Send correspondence to: Jasjit S. Suri, Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc. Roseville, CA, USA, Tel: 916-749-5628, Fax: 916-749-4942, E-mail: jsuri@comcast.net
Front. Biosci. (Landmark Ed) 2020, 25(2), 299–334; https://doi.org/10.2741/4808
Published: 1 January 2020
Abstract

Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classified as low and high risk regions using a backpropagation neural network (BPNN). However, this approach yielded low performance and stability necessitating development of a more robust model. We hypothesized that by spiking neuron models in simulating the characteristics of a neuron, which when embedded with a BPNN, could improve the performance for the assessment of malaria prone regions. To this end, we created an inter-spike interval (ISI)-based BPNN (ISI-BPNN) architecture that uses a single-pass spiking learning strategy and has a parallel structure that is useful for non-linear regression tasks. Existing malaria dataset comprised of 1296 records, that met these attributes, were used. ISI-BPNN showed superior performance, and a high accuracy. The benchmarking results showed reliability and stability and an improvement of 11.9% against a multilayer perceptron and 9.19% against integrate-and-fire neuron models. The ISI-BPNN model is well suited for deciphering the risk of acquiring malaria as well as other diseases in prone regions of the world.

Keywords
Malaria
Integrate-And-Fire Neuron
Backpropagation
Spiking Neural Network
Interspike Interval
Machine Learning Algorithm
Performance
Figures
Figure 1
Share
Back to top