Affiliation:
1. School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
2. School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China
Abstract
Warts are a prevalent condition worldwide, affecting approximately 10% of the global population. In this study, a machine learning method based on a dendritic neuron model is proposed for wart-treatment efficacy prediction. To prevent premature convergence and improve the interpretability of the model training process, an effective heuristic algorithm, i.e., the covariance matrix adaptation evolution strategy (CMA-ES), is incorporated as the training method of the dendritic neuron model. Two common datasets of wart-treatment efficacy, i.e., the cryotherapy dataset and the immunotherapy dataset, are used to verify the effectiveness of the proposed method. The proposed CMA-ES-based dendritic neuron model achieves promising results, with average classification accuracies of 0.9012 and 0.8654 on the two datasets, respectively. The experimental results indicate that the proposed method achieves better or more competitive prediction results than six common machine learning models. In addition, the trained dendritic neuron model can be simplified using a dendritic pruning mechanism. Finally, an effective wart-treatment efficacy prediction method based on a dendritic neuron model, which can provide decision support for physicians, is proposed in this paper.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
1 articles.
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