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DOI: 10.14569/IJACSA.2020.0110379
PDF

Binning Approach based on Classical Clustering for Type 2 Diabetes Diagnosis

Author 1: Hai Thanh Nguyen
Author 2: Nhi Yen Kim Phan
Author 3: Huong Hoang Luong
Author 4: Nga Hong Cao
Author 5: Hiep Xuan Huynh

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 3, 2020.

  • Abstract and Keywords
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Abstract: In recent years, numerous studies have been fo-cusing on metagenomic data to improve the ability of human disease prediction. Although we face the complexity of disease, some proposed frameworks reveal promising performances in using metagenomic data to predict disease. Type 2 diabetes (T2D) diagnosis by metagenomic data is one of the challenging tasks compared to other diseases. The prediction performances for T2D usually reveal poor results which are around 65% in accuracy in state-of-the-art. In this study, we propose a method com-bining K-means clustering algorithm and unsupervised binning approaches to improve the performance in metagenome-based disease prediction. We illustrate by experiments on metagenomic datasets related to Type 2 Diabetes that the proposed method embedded clusters generated by K-means allows to increase the performance in prediction accuracy reaching approximately or more than 70%.

Keywords: Unsupervised binning; K-means clustering algo-rithm; metagenomics; metagenome-based disease prediction; Type 2 diabetes diagnosis

Hai Thanh Nguyen, Nhi Yen Kim Phan, Huong Hoang Luong, Nga Hong Cao and Hiep Xuan Huynh, “Binning Approach based on Classical Clustering for Type 2 Diabetes Diagnosis” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110379

@article{Nguyen2020,
title = {Binning Approach based on Classical Clustering for Type 2 Diabetes Diagnosis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110379},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110379},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Hai Thanh Nguyen and Nhi Yen Kim Phan and Huong Hoang Luong and Nga Hong Cao and Hiep Xuan Huynh}
}



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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