IMR Press / FBL / Volume 25 / Issue 7 / DOI: 10.2741/4853

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 as a courtesy and upon agreement with Frontiers in Bioscience.

An AI-based approach in determining the effect of meteorological factors on incidence of malaria
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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:
Front. Biosci. (Landmark Ed) 2020, 25(7), 1202–1229;
Published: 1 March 2020

This study presents the classification of malaria-prone zones based on (a) meteorological factors, (b) demographics and (c) patient information. Observations are performed on extended features in dataset over the spiking and non-spiking classifiers including Quadratic Integrate and Fire neuron (QIFN) model as a benchmark. As per research studies, parasite transmission is highly dependent on the (i) stagnant water, (ii) population of area and the (iii) greenery of the locality. Considering these factors, three more attributes were added to the existing novel dataset and comparison on the results is presented. For four feature dataset, QIFN exhibited an accuracy of 97.08% in K10 protocol, and with extended dataset; QIFN yields an accuracy of 99.58% in K10 protocol. The benchmarking results showed reliability and stability. There is 12.47% improvement against multilayer perceptron (MLP) and 5.39% against integrate-and-fire neuron (IFN) model. The QIFN model performed the best over the conventional classifiers for deciphering the risk of acquiring malaria in different geographical regions worldwide.

Quadratic Integrate-and-Fire Neuron
Spiking Neural Network
Machine Learning Algorithm
Artificial Neural Networks
Figure 1
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