SAPNN: self-adaptive probabilistic neural network for medical diagnosis

被引：0

作者：

Xiong, Yibin ^{[1
]}

Wu, Jun ^{[2
]}

Wang, Qian ^{[2
]}

Wei, Dandan ^{[2
]}

机构：

[1] Hunan Inst Sci & Technol, Sch Informat Sci & Technol, Yueyang, Peoples R China

[2] Zunyi Normal Univ, Sch Informat Engn, Zunyi, Peoples R China

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING | 2024年 / 27卷 / 01期

基金：

中国国家自然科学基金;

关键词：

ancillary diagnosis of disease; cuckoo search; information sharing; mutation strategy; probabilistic neural network; CUCKOO SEARCH; ALGORITHM;

D O I：

10.1504/IJCSE.2024.136252

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A self-adaptive probabilistic neural network (SAPNN) is proposed in this paper. Firstly, a hybrid cuckoo search (HCS) algorithm is proposed. Secondly, HCS is used in probabilistic neural networks for adapting the smoothing factor parameters. In order to accurately evaluate SAPNN proposed in this paper, the disease datasets of breast cancer, diabetes and Parkinson's disease were used for testing. Finally, comparison with several other methods shows that the accuracy of SAPNN is the best in all cases. The results of various evaluation indexes show that the proposed SAPNN in this paper is a novel method that can be applied to medical diagnosis.

引用

页码：68 / 77

页数：11

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