Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model

In view of the harm of diabetes to the population, we have introduced an ensemble learning algorithm—EXtreme Gradient Boosting (XGBoost) to predict the risk of type 2 diabetes and compared it with Support Vector Machines (SVM), the Random Forest (RF) and K-Nearest Neighbor (K-NN) algorithm in order...

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Detalles Bibliográficos
Autores principales: Wang, Liyang, Wang, Xiaoya, Chen, Angxuan, Jin, Xian, Che, Huilian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7551910/
https://www.ncbi.nlm.nih.gov/pubmed/32751894
http://dx.doi.org/10.3390/healthcare8030247

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7551910/
https://www.ncbi.nlm.nih.gov/pubmed/32751894
http://dx.doi.org/10.3390/healthcare8030247

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