| Title |
A Data Augmentation Methodology for Predicting the Association of Microbiome Community and Diseases Based on Artificial Intelligence |
| Authors |
이영지(Young-Ji Lee) ; 박준형(Jun-hyung Park) ; 정호용(Ho-yong Chung) ; 김광민(Kwang-min Kim) ; 이승호(Seung-Ho Lee) |
| DOI |
https://doi.org/10.5573/ieie.2021.58.3.59 |
| Keywords |
Data Augmentation; Microbiome Community; Deep Learning Network; Genus; Species; Disease Prediction |
| Abstract |
In this paper, we propose a data augmentation methodology for predicting the association of microbiome community and diseases based on artificial intelligence. Based on the microbiome community data provided, Jittering, Scaling, Permutation, Magnitude Warning, etc. are performed and expanded learning data are built to enhance the performance of deep learning. As a result of the experiment, the data augmentation methodology by Jittering showed the highest disease prediction accuracy accuracy for the learning sets and test sets. Using the proposed data augmentation methodology in this paper, it can be useful in predicting the association of microbiome community and diseases based on artificial intelligence. |