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政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 > Item 140.119/140950

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/140950

Title:	A Hybrid Neural Network based on Particle Swarm Optimization for Predicting the Diabetes
Authors:	楊亨利;李博逸 Yang, Heng-Li;Li, Bo-Yi
Contributors:	資管系
Keywords:	Particle Swarm Optimization;Neural Network;Diabetes Dataset
Date:	2021-02
Issue Date:	2022-07-29 15:44:09 (UTC+8)
Abstract:	In recent years, more and more studies have applied hybrid models in order to improve the performance of traditional neural networks. By combining particle swarm optimization and neural network, this research proposes a new hybrid neural prediction algorithm named as PSONN. The algorithm was applied to Pima Indians Diabetes Database and compared with eight other algorithms including Logistic regression, Ridge regression, Lasso regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Random Forest, Gradient Boosting Machine, and Adam (a neural network algorithm). The findings indicated that the proposed algorithm had higher accuracy and stability, but it took more time to execute. It is suggested that, future research could apply parallelization technology for reducing execution time.
Relation:	ICSCA 2021: 2021 10th International Conference on Software and Computer Applications, Association for Computing Machinery, ISBN: 9781450388825, pages 302-306
Data Type:	conference
DOI 連結:	https://doi.org/10.1145/3457784.3457831
DOI:	10.1145/3457784.3457831
Appears in Collections:	[資訊管理學系] 會議論文

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