Explainable artificial intelligence-driven gestational diabetes mellitus prediction using clinical and laboratory markers-Reference-Cited by-同舟云学术

Explainable artificial intelligence-driven gestational diabetes mellitus prediction using clinical and laboratory markers

Published:2024-03-26 Issue:1 Volume:11 Page:
ISSN:2331-1916
Container-title:Cogent Engineering
language:en
Short-container-title:Cogent Engineering

Author:

Vivek Khanna Varada¹^ORCID,Chadaga Krishnaraj²^ORCID,Sampathila Niranjana¹^ORCID,Prabhu Srikanth²^ORCID,Chadaga P. Rajagopala³,Bhat Devadas¹,K. S. Swathi⁴^ORCID

Affiliation:

1. Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

2. Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

3. Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

4. Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23311916.2024.2330266

Reference47 articles.

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2. Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., & Ridella, S. (2012, April 25–27). The ‘K’ in K-fold cross validation. In ESANN 2012 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium (pp. 441–446). i6doc Publishers.

3. Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory

4. Hybrid Prediction Model for Type-2 Diabetes Mellitus using Machine Learning Approach

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