Improving Classification Performance on Imbalance Medical Data using Generative Adversarial Network
Dublin Core
Title
Improving Classification Performance on Imbalance Medical Data using Generative Adversarial Network
Subject
Classification, GAN, Imbalance, Machine Learning, Oversampling
Description
In many real-world applications, the problem of data imbalance is a common challenge that
significantly affects the performance of machine learning algorithms. Data imbalance means each
target of classes is not balanced. This problem often appears in medical data, where the positive cases
of a disease or condition are much fewer than the negative cases. In this paper, we propose to explore
the oversampling-based Generative Adversarial Networks (GAN) method to improve the performance of the classification algorithm over imbalanced medical datasets. We expect that GAN will be able to learn the actual data distribution and generate synthetic samples that are similar to the original ones. We evaluate our proposed methods on several metrics: Recall, Precision, F1 score, AUC score, and FP rate. These metrics measure the ability of the classifier to correctly identify the minority class and reduce the false positives and false negatives. Our experimental results show that the application of GAN performs better than other methods in several metrics across datasets and can be used as an alternative method to improve the performance of the classification model on imbalanced medical data.
significantly affects the performance of machine learning algorithms. Data imbalance means each
target of classes is not balanced. This problem often appears in medical data, where the positive cases
of a disease or condition are much fewer than the negative cases. In this paper, we propose to explore
the oversampling-based Generative Adversarial Networks (GAN) method to improve the performance of the classification algorithm over imbalanced medical datasets. We expect that GAN will be able to learn the actual data distribution and generate synthetic samples that are similar to the original ones. We evaluate our proposed methods on several metrics: Recall, Precision, F1 score, AUC score, and FP rate. These metrics measure the ability of the classifier to correctly identify the minority class and reduce the false positives and false negatives. Our experimental results show that the application of GAN performs better than other methods in several metrics across datasets and can be used as an alternative method to improve the performance of the classification model on imbalanced medical data.
Creator
Siska Rahmadani, Agus Subekti, Muhammad Haris
Source
Ihttp://dx:doi:org/10:21609/jiki:v17i1.1177
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2024-02-25
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Siska Rahmadani, Agus Subekti, Muhammad Haris, “Improving Classification Performance on Imbalance Medical Data using Generative Adversarial Network,” Repository Horizon University Indonesia, accessed May 22, 2025, https://repository.horizon.ac.id/items/show/8863.