i Effects of meteorological factors on the prediction of malaria incidences AI-based approaches i

Autor:	Ajeet Kumar Verma, Saurabh Kr. Srivastava, Venkatanareshbabu Kuppili, Jasjit S. Suri
Rok vydání:	2020
Předmět:	education.field_of_study Feature Dataset Computer science Multilayer perceptron Incidence (epidemiology) Reliability (computer networking) Population Statistics Benchmark (computing) Stability (learning theory) Benchmarking education
Zdroj:	Frontiers in Bioscience. 25:1202-1229
ISSN:	1093-4715 1093-9946
DOI:	10.2741/4853
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f9bc558d60bfa69aa67a66aee3be5c0f https://doi.org/10.2741/4853 Zobrazit plný text záznamu