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DOI: 10.14569/IJACSA.2020.0110778
PDF

Enhancing Disease Prediction on Imbalanced Metagenomic Dataset by Cost-Sensitive

Author 1: Hai Thanh Nguyen
Author 2: Toan Bao Tran
Author 3: Quan Minh Bui
Author 4: Huong Hoang Luong
Author 5: Trung Phuoc Le
Author 6: Nghi Cong Tran

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 7, 2020.

  • Abstract and Keywords
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Abstract: Imbalanced datasets usually appear popularly to many real-world applications and studies. For metagenomic data, we also face the same issue where the number of patients is greater than the number of healthy individuals or vice versa. In this study, we propose a method to handle the imbalanced datasets issues by Cost-sensitive approach. The proposed method is evaluated on an imbalanced metagenomic dataset related to Inflammatory bowel disease to do prediction tasks. Our method reaches a noteworthy improvement on prediction performance with deep learning algorithms including a MultiLayer Perceptron and a Convolutional Neural Neural Network with the proposed cost-sensitive for Metagenome-based Disease Prediction tasks.

Keywords: Cost-sensitive; imbalanced datasets; disease predic-tion; deep learning

Hai Thanh Nguyen, Toan Bao Tran, Quan Minh Bui, Huong Hoang Luong, Trung Phuoc Le and Nghi Cong Tran, “Enhancing Disease Prediction on Imbalanced Metagenomic Dataset by Cost-Sensitive” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110778

@article{Nguyen2020,
title = {Enhancing Disease Prediction on Imbalanced Metagenomic Dataset by Cost-Sensitive},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110778},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110778},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Hai Thanh Nguyen and Toan Bao Tran and Quan Minh Bui and Huong Hoang Luong and Trung Phuoc Le and Nghi Cong Tran}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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