Title: SAPNN: self-adaptive probabilistic neural network for medical diagnosis

Authors: Yibin Xiong; Jun Wu; Qian Wang; Dandan Wei

Addresses: School of Information Science and Technology, Hunan Institute of Science and Technology, Yueyang, China ' School of Information Engineering, Zunyi Normal University, Zunyi, China ' School of Information Engineering, Zunyi Normal University, Zunyi, China ' School of Information Engineering, Zunyi Normal University, Zunyi, China

Abstract: 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.

Keywords: ancillary diagnosis of disease; cuckoo search; information sharing; mutation strategy; probabilistic neural network.

DOI: 10.1504/IJCSE.2024.136252

International Journal of Computational Science and Engineering, 2024 Vol.27 No.1, pp.68 - 77

Received: 11 Jul 2022
Received in revised form: 05 Aug 2022
Accepted: 08 Sep 2022
Published online: 25 Jan 2024 *

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