# Part-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model
## 5 Conclusions  
To help in the selection of appropriate treatment methods for patients and improve the accuracy of wart-treatment efficacy prediction, in this study, we constructed a wart-treatment efficacy prediction method based on an improved DNM. The covariance matrix adaptation evolution strategy was combined with the DNM to improve the performance of the DNM while taking into account the interpretability of the optimization process. Due to the sample imbalance in the original dataset, a focal loss function was introduced to address the problem of bias in the generated model toward the majority of samples. Two common datasets of wart-treatment efficacy, the cryotherapy dataset and the immunotherapy dataset, were employed as the benchmark datasets. The proposed CMA-ES-based dendritic neuron model achieved promising results, with average classification accuracies of 0.9012 and 0.8654 on the two datasets, respectively. The superiority of the proposed method was demonstrated by comparing it with six popular machine learning models. Based on the specific pruning mechanism, the structure of the trained DNM can be greatly simplified. The proposed method can help physicians make decisions and is a promising technique that can be integrated into a clinical decision-support system. This study emphasized the importance of artificial intelligence technology in improving medical treatments.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Loss Function** & **Original DNM** & **Pruned DNM** \\ \hline \multirow{2}{*}{Cryotherapy} & MSE & \(0.8894\pm 0.04\) & \(0.8841\pm 0.03\) \\  & FL & \(0.9012\pm 0.05\) & \(0.8932\pm 0.04\) \\ \hline \multirow{2}{*}{Immunotherapy} & MSE & \(0.8404\pm 0.05\) & \(0.8366\pm 0.06\) \\  & FL & \(0.8654\pm 0.03\) & \(0.8611\pm 0.04\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the classification accuracies of the original DNM and the pruned DNM.  
Figure 7: The typical structures of the pruned DNM for the two datasets.  
Nevertheless, this study also has the following limitations. First, more datasets of wart-treatment efficacy can be employed to verify the effectiveness of the proposed method. Second, since we do not provide a software suite to implement the DNM, it is not easy to integrate the proposed method into a clinical decision-support system.  
In our future work, more comprehensive patient data will be incorporated into the DNM to enhance its generalization ability. Applying the DNM in computer-aided diagnosis will also be a focus of our future efforts.  
Conceptualization, S.S. and B.Z.; methodology, S.S. and B.Z.; software, B.Z. and Q.X.; validation, X.C., Q.X. and J.Q.; formal analysis, B.Z. and Q.X.; investigation, S.S. and J.Q.; resources, S.S. and J.Q.; data curation, S.S. and B.Z.; writing--original draft preparation, S.S. and B.Z.; writing--review and editing, X.C. and J.Q.; visualization, B.Z.; supervision, S.S. and X.C.; project administration, S.S. and J.Q.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 62203069) and the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20220619).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data are contained within the article.  
The authors declare no conflict of interest.