SMOTE Oversampling and Near Miss Undersampling Based Diabetes Diagnosis from Imbalanced Dataset with XAI Visualization-Reference-Cited by-同舟云学术

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SMOTE Oversampling and Near Miss Undersampling Based Diabetes Diagnosis from Imbalanced Dataset with XAI Visualization

Published:2023-07-09 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Symposium on Computers and Communications (ISCC)
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Author:

Nayan Nasim Mahmud¹,Islam Ashraful²,Islam Muhammad Usama³,Ahmed Eshtiak²,Hossain Mohammad Mobarak¹,Alam Md Zahangir⁴

Affiliation:

1. University of Information Technology and Sciences,Department of Computer Science and Engineering,Bangladesh

2. Independent University,Center for Computational and Data Sciences,Bangladesh

3. School of Computing and Informatics, University of Louisiana at Lafayette,USA

4. Independent University,Department of Computer Science and Engineering,Bangladesh

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10217219/10217829/10218281.pdf?arnumber=10218281

Reference14 articles.

1. Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

2. The Past, Present, and Prospective Future of XAI: A Comprehensive Review

3. Diabetes prediction and analysis using medical attributes: A machine learning approach;rajput;Xian University of Architecture and Technology Journal,2022

4. Under-Sampling Approaches for Improving Prediction of the Minority Class in an Imbalanced Dataset

5. SMOTE: Synthetic Minority Over-sampling Technique

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