This paper is in the following e-collection/theme issue:

Population and Public Health Informatics (106) Artificial Intelligence in Diabetes Care and Prevention (29)

Published on in Vol 12, No 1 (2020):

Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet

Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet

Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet

Authors of this article:

Riyad Alshammari1 ;   Noorah Atiyah ;   Tahani Daghistani1 ;   Abdulwahhab Alshammari1
The full text of this article is available as a PDF download by clicking here.
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Journals

  1. Samet S, Laouar M, Bendib I, Eom S. Analysis and Prediction of Diabetes Disease Using Machine Learning Methods. International Journal of Decision Support System Technology 2022;14(1):1 View
  2. Chang L, Fukuoka Y, Aouizerat B, Zhang L, Flowers E. Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes. Biological Research For Nursing 2023;25(3):393 View
  3. Xiao M, Lu C, Ta N, Wei H, Yang C, Wu H. Toe PPG sample extension for supervised machine learning approaches to simultaneously predict type 2 diabetes and peripheral neuropathy. Biomedical Signal Processing and Control 2022;71:103236 View
  4. Daza A, Ponce Sánchez C, Apaza-Perez G, Pinto J, Zavaleta Ramos K. Stacking ensemble approach to diagnosing the disease of diabetes. Informatics in Medicine Unlocked 2024;44:101427 View

Books/Policy Documents

  1. Samet S, Laouar M, Bendib I. 12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022”. View