Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha.
This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test options, 10-fold cross-validation, percentile split,...
| Publicado en: | International Journal of Environmental Health Research Vol. 32; no. 8; pp. 1716 - 1733 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Aug2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157791807&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157791807 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09603123 57L jtl: International Journal of Environmental Health Research issn: 09603123 maglogo: Y pubinfo: dt: Aug2022 vid: 32 iid: 8 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 157791807 149485612 157791807 157791807 10.1080/09603123.2021.1905782 157791807 ppf: 1716 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha. aug: au: Mohapatra, Pallavi Tripathi, N. K. Pal, Indrajit Shrestha, Sangam affil: Remote Sensing and Geographic Information System, Asian Institute of Technology, Pathum Thani, Thailand sug: subj: Malaria Epidemiology Risk Assessment Machine Learning Utilization Malaria Risk Factors Climate India Human Decision Trees Comparative Studies Incidence ab: This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test options, 10-fold cross-validation, percentile split, and supplied test. A comparative analysis was carried out to ascertain the superior model among malaria prediction accuracy techniques in varying climate contexts. The results suggested that J48 had exhibited better skill than MLP with the 10-fold cross-validation method over the percentile split and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Seasonal variation of temperature and humidity had a better association with malaria incidents than rainfall, and the performance was better during the monsoon and post-monsoon when the incidents are at the peak. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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