Wart-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model

نویسندگان

چکیده

Warts are a prevalent condition worldwide, affecting approximately 10% of the global population. In this study, machine learning method based on dendritic neuron model is proposed for wart-treatment efficacy prediction. To prevent premature convergence and improve interpretability training process, an effective heuristic algorithm, i.e., covariance matrix adaptation evolution strategy (CMA-ES), incorporated as model. Two common datasets efficacy, cryotherapy dataset immunotherapy dataset, used to verify effectiveness method. The CMA-ES-based achieves promising results, with average classification accuracies 0.9012 0.8654 two datasets, respectively. experimental results indicate that better or more competitive prediction than six models. addition, trained can be simplified using pruning mechanism. Finally, model, which provide decision support physicians, in paper.

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2023

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app13116542