A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study.

Sampa, Masuda Begum; Biswas, Topu; Rahman, Md Siddikur; Aziz, Nor Hidayati Binti Abdul; Hossain, Md Nazmul; Aziz, Nor Azlina Ab

Sampa, Masuda Begum; Biswas, Topu; Rahman, Md Siddikur; Aziz, Nor Hidayati Binti Abdul; Hossain, Md Nazmul; Aziz, Nor Azlina Ab.

Afiliación

Sampa MB; Center for Engineering Computational Intelligence, Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.
Biswas T; Department of Computer Science and Engineering, Faculty of Science, Engineering and Technology, University of Science and Technology Chittagong, Chattogram, Bangladesh.
Rahman MS; Department of Computer Science and Engineering, Faculty of Science, Engineering and Technology, University of Science and Technology Chittagong, Chattogram, Bangladesh.
Aziz NHBA; Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur, Bangladesh.
Hossain MN; Center for Engineering Computational Intelligence, Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.
Aziz NAA; Department of Marketing, Faculty of Business Studies, University of Dhaka, Dhaka, Bangladesh.

JMIR Diabetes ; 8: e49113, 2023 Nov 24.

Article en En | MEDLINE | ID: mdl-37999944

Palabras clave

blood glucose prediction; boosted decision tree regression model; machine learning; noncommunicable diseases; noninvasive

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: JMIR Diabetes Año: 2023 Tipo del documento: Article País de afiliación: Malasia Pais de publicación: Canadá

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