文章基本信息

标题：Enhancing Disease Prediction on Imbalanced Metagenomic Dataset by Cost-Sensitive
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作者：Hai Thanh Nguyen ; Toan Bao Tran ; Quan Minh Bui 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：7
DOI：10.14569/IJACSA.2020.0110778
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Cost-sensitive; imbalanced datasets; disease predic-tion; deep learning