Explainable data mining model for hyperinsulinemia diagnostics-Reference-Cited by-同舟云学术

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Explainable data mining model for hyperinsulinemia diagnostics

Published:2024-03-04 Issue:1 Volume:36 Page:
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Rankovic Nevena¹^ORCID,Rankovic Dragica²^ORCID,Ivanovic Mirjana³^ORCID,Lukic Igor⁴^ORCID

Affiliation:

1. Department of Cognitive Science and Artificial Intelligence, Tilburg Univeristy, Tilburg, The Netherlands

2. Department of Mathematics, Informatics and Statistics, Union University “Nikola Tesla”, Nis, Serbia

3. Department of Mathematics and Informatics, University of Novi Sad, Novi Sad, Serbia

4. Department of Preventive Medicine, University of Kragujevac, Kragujevac, Serbia

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2024.2325496

Reference61 articles.

1. Deep and hybrid learning of MRI diagnosis for early detection of the progression stages in Alzheimer’s disease

2. Alam, A., & Muqeem, M. (2022, March). Integrated k-means clustering with nature inspired optimization algorithm for the prediction of disease on high dimensional data. In 2022 international conference on electronics and renewable systems (ICEARS) (pp. 1556–1561), Organized by: St. Mother Theresa Engineering College. IEEE.

3. A Random Forest based predictor for medical data classification using feature ranking

4. Prevalence of Prediabetes Among Adolescents and Young Adults in the United States, 2005-2016

5. Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)

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