DiGAN Breakthrough: Advancing diabetic data analysis with innovative GAN-based imbalance correction techniques
Published in Computer Methods and Programs in Biomedicine Update, 2024
Recommended citation: Zhao, P., Liu, X., Yue, Z., Zhao, Q., Liu, X., Deng, Y., & Wu, J. (2024). DiGAN Breakthrough: Advancing diabetic data analysis with innovative GAN-based imbalance correction techniques. Computer Methods and Programs in Biomedicine Update, 5, 100152. http://qianyuzhao.github.io/files/DiGAN.pdf
This paper uniquely applies Generative Adversarial Network, traditionally used in image processing, to diabetes data analysis and classification, achieving a weighted F1 score of 90%, a 20% improvement over traditional methods.
Recommended citation: Zhao, P., Liu, X., Yue, Z., Zhao, Q., Liu, X., Deng, Y., & Wu, J. (2024). DiGAN Breakthrough: Advancing diabetic data analysis with innovative GAN-based imbalance correction techniques. Computer Methods and Programs in Biomedicine Update, 5, 100152.