Specialist hybrid models with asymmetric training for malaria prevalence prediction.

Fisher, Thomas; Rojas-Galeano, Sergio; Fernandez-Reyes, Delmiro

Fisher, Thomas; Rojas-Galeano, Sergio; Fernandez-Reyes, Delmiro.

Affiliation

Fisher T; Department of Computer Science, Faculty of Engineering Sciences, University College London, London, United Kingdom.
Rojas-Galeano S; Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia.
Fernandez-Reyes D; Department of Computer Science, Faculty of Engineering Sciences, University College London, London, United Kingdom.

Front Public Health ; 11: 1207624, 2023.

Article in En | MEDLINE | ID: mdl-37808978

Subject(s)

Malaria; Neural Networks, Computer; Humans; Prevalence; Nigeria/epidemiology; Algorithms; Malaria/epidemiology

Key words

ML hybrid models; asymmetrical loss; machine learning; malaria prevalence prediction; recurrent network models

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Full text: 1 Database: MEDLINE Main subject: Neural Networks, Computer / Malaria Type of study: Prevalence_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Country/Region as subject: Africa Language: En Journal: Front Public Health Year: 2023 Type: Article Affiliation country: United kingdom

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