Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods.

Sadeghi, Somayeh; Khalili, Davood; Ramezankhani, Azra; Mansournia, Mohammad Ali; Parsaeian, Mahboubeh

Sadeghi, Somayeh; Khalili, Davood; Ramezankhani, Azra; Mansournia, Mohammad Ali; Parsaeian, Mahboubeh.

Afiliação

Sadeghi S; Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, P.O. Box 14155-6446, Tehran, Iran.
Khalili D; Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Ramezankhani A; Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mansournia MA; Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Parsaeian M; Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, P.O. Box 14155-6446, Tehran, Iran. mansournia_ma@yahoo.com.

BMC Med Inform Decis Mak ; 22(1): 36, 2022 02 10.

Article em En | MEDLINE | ID: mdl-35139846

Assuntos

Diabetes Mellitus; Aprendizado de Máquina; Algoritmos; Humanos; Irã (Geográfico); Redes Neurais de Computação

Palavras-chave

Cost-sensitive learning; Diabetes mellitus; Imbalanced data; Machine learning; Sampling strategies

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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Diabetes Mellitus / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Humans País/Região como assunto: Asia Idioma: En Revista: BMC Med Inform Decis Mak Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Irã

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