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prediction

A novel model for malaria prediction based on ensemble algorithms

January 15, 2020 - 08:56 -- Open Access
Author(s): 
Wang M, Wang H, Wang J, Liu H, Lu R, Duan T, Gong X, Feng S, Liu Y, Cui Z, Li C, Ma J
Reference: 
PLoS ONE 14(12): e0226910

Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction.

A new backpropagation neural network classification model for prediction of incidence of malaria

January 7, 2020 - 14:28 -- Open Access
Author(s): 
Verma AK, Kuppili V, Srivastava SK, Suri JS
Reference: 
Frontiers in Bioscience, Elite, 25, 299-334, Jan 1, 2020

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.

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