Efficient diabetes mellitus prediction with grid based random forest classifier in association with natural language processing-Reference-Cited by-同舟云学术

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Efficient diabetes mellitus prediction with grid based random forest classifier in association with natural language processing

Published:2021-03-25 Issue:3 Volume:24 Page:601-614
ISSN:1381-2416
Container-title:International Journal of Speech Technology
language:en
Short-container-title:Int J Speech Technol

Author:

Abokhzam Asma Ahmed,Gupta N. K.,Bose Dipak Kumar

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Link

https://link.springer.com/content/pdf/10.1007/s10772-021-09825-z.pdf

Reference26 articles.

1. Alberti, K. G., & Zimmet, P. Z. (1998). Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabetic Medicine, 15(7), 539–553. https://doi.org/10.1002/(SICI)1096-9136(199807)15:7%3c539::AID-DIA668%3e3.0.CO;2-S

2. American Diabetes Association. (2012). Diagnosis and classification of diabetes mellitus. Diabetes Care, 35, S64–S71. https://doi.org/10.2337/dc12-s064

3. Chen, W., Chen, S., & Zhang, H. (2017). A hybrid prediction model for type 2 diabetes using machine learning and decision tree algorithms. National herbal basis of technology in China, IEEE, 5386-0497.

4. Cherifi, E. H., et al. (2021). Arabic grapheme-to-phoneme conversion based on joint multi-gram model. International Journal of Speech Technology, 24, 173–182.

5. Contreras, I., & Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: Literature review. Journal of Medical Internet Research, 20(5), e10775. https://doi.org/10.2196/10775

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