# Part-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model
## 4 Experimental Studies
### Comparison with Classic Machine Learning Models  
To further verify the effectiveness of the proposed models, the DNM-FL and DNM-MSE were compared with six popular machine learning models, including multilayer perceptron (MLP), Bayesian classifier (Bayes), support vector machine (SVM), Ada boosting (Ada), K-nearest neighbor (KNN), and decision tree (DT). The hyperparameters of these machine learning models were set as described in Table 8. Six metrics (accuracy, sensitivity, specificity, precision, F1 score value, and AUC value) were used to evaluate each model. The above 6 metrics were averaged from 30 independent experiments. The \(p\)-value corresponds to the Wilcoxon signed rank test, which was used to determine the significant differences in accuracy between the DNM-FL and other machine learning models. The confidence level was set at 0.05. Table 9 shows the performance of the eight machine learning models on the two datasets. \begin{table}
\begin{tabular}{l l c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Loss Function**} \\ \cline{3-5}  & & **MSE** & **FL** \\ \hline \multirow{5}{*}{Cryotherapy} & CMA-ES & \(3.57\times 10^{-2}\pm 5.32\times 10^{-3}\) & \(5.37\times 10^{-2}\pm 8.41\times 10^{-3}\) \\  & DE & \(5.35\times 10^{-2}\pm 6.02\times 10^{-3}\) & \(8.86\times 10^{-2}\pm 6.82\times 10^{-3}\) \\  & PSO & \(4.76\times 10^{-2}\pm 1.48\times 10^{-2}\) & \(9.21\times 10^{-2}\pm 3.26\times 10^{-2}\) \\  & GA & \(7.59\times 10^{-2}\pm 1.27\times 10^{-2}\) & \(8.54\times 10^{-2}\pm 3.28\times 10^{-2}\) \\  & HHO & \(7.24\times 10^{-2}\pm 2.69\times 10^{-2}\) & \(8.90\times 10^{-2}\pm 1.74\times 10^{-2}\) \\  & BP & \(5.55\times 10^{-2}\pm 2.17\times 10^{-2}\) & \(9.33\times 10^{-2}\pm 4.61\times 10^{-2}\) \\ \hline \multirow{5}{*}{Immunotherapy} & CMA-ES & \(4.69\times 10^{-2}\pm 8.68\times 10^{-3}\) & \(1.00\times 10^{-1}\pm 2.08\times 10^{-2}\) \\  & DE & \(4.25\times 10^{-2}\pm 3.76\times 10^{-3}\) & \(9.15\times 10^{-2}\pm 1.25\times 10^{-2}\) \\ \cline{1-1}  & PSO & \(3.87\times 10^{-2}\pm 1.45\times 10^{-2}\) & \(1.08\times 10^{-1}\pm 2.51\times 10^{-2}\) \\ \cline{1-1}  & GA & \(4.69\times 10^{-2}\pm 1.05\times 10^{-2}\) & \(1.14\times 10^{-1}\pm 2.62\times 10^{-2}\) \\ \cline{1-1}  & HHO & \(4.24\times 10^{-2}\pm 1.62\times 10^{-2}\) & \(1.01\times 10^{-1}\pm 2.10\times 10^{-2}\) \\ \cline{1-1}  & BP & \(6.63\times 10^{-2}\pm 1.83\times 10^{-2}\) & \(2.04\times 10^{-1}\pm 7.70\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the convergence results of the six optimization algorithms. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Dataset** & **Loss Function** & **CMA-ES** & **DE** & **PSO** & **GA** & **HHO** & **BP** \\ \hline \multirow{2}{*}{Cryotherapy} & MSE & 0.8894 & 0.8667 & 0.6649 & 0.8070 & 0.8238 & 0.7904 \\  & FL & 0.9012 & 0.8368 & 0.6894 & 0.7403 & 0.8333 & 0.8388 \\ \hline \multirow{2}{*}{Immunotherapy} & MSE & 0.8404 & 0.8350 & 0.7403 & 0.8157 & 0.8023 & 0.8047 \\  & FL & 0.8654 & 0.8526 & 0.7263 & 0.7561 & 0.8357 & 0.8166 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the classification accuracies of the six optimization algorithms. Based on the results of the comparison experiments presented in Table 9, it is evident that the DNM-FL achieved superior results on the cryotherapy dataset, with values of 0.9012, 0.8964, 0.8919, 0.9302, 0.9068, and 0.9630 for accuracy, sensitivity, specificity, precision, F1 score, and AUC value, respectively. Ada boosting exhibited the highest specificity of 0.8961 among all the classifiers. Furthermore, the DNM-MSE demonstrated higher accuracy than the other non-DNM classifiers, indicating that the performance of the DNM optimized using the CMA-ES was significantly improved. The DNM-FL outperformed all the compared classifiers in all metrics, except for specificity. As the cryotherapy dataset's samples were relatively balanced, there was no significant difference between the performance of the DNM-FL and DNM-MSE.