An Integrated Classification and Association Rule Technique for Early-Stage Diabetes Risk Prediction.

Khafaga, Doaa Sami; Alharbi, Amal H; Mohamed, Israa; Hosny, Khalid M

Khafaga, Doaa Sami; Alharbi, Amal H; Mohamed, Israa; Hosny, Khalid M.

Afiliação

Khafaga DS; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Alharbi AH; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Mohamed I; Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt.
Hosny KM; Faculty of Engineering and Computer Sciences, King Salman International University, Tor Sinai 46512, Egypt.

Healthcare (Basel) ; 10(10)2022 Oct 18.

Article em En | MEDLINE | ID: mdl-36292517

Palavras-chave

BBC; LOF; association rules and healthcare data analytics; classification algorithms; data mining; disease prediction; early-stage diabetes

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Healthcare (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Arábia Saudita

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