## 3 Results
### Multilayer Perceptron (MLP) Model  
In this study, we utilized the MLP method to analyze the dataset presented in Table 1 and identified effectiveness factors, such as VHI scores, that may be associated with voice treatment outcomes. The details of the MLP model as used for this study are provided in Table 8. About 75% of the entire dataset was used for training, with the remaining 25% allocated for testing. The MLP model was designed to predict improvements in VHI scores based on a variety of input factors, including gender, smoking status, alcohol consumption, voice usage, coffee consumption, comorbidity status, surgical history, and voice training status. The input layer consists of seventeen units; the hidden layer is composed of a single layer with two units and employs the hyperbolic tangent activation function; and the output layer, which includes two units, utilizes the softmax activation function. The model's optimization is guided via the cross-entropy error function. The MLP model's performance was assessed using several metrics, including accuracy, sensitivity, specificity, F score, and the area under the curve (AUC) [5, 27]. Figure 6 illustrates a receiver operating characteristic (ROC) curve, a widely used tool for assessing the performance of binary classification models. The y-axis represents sensitivity, or the true-positive rate, while the x-axis depicts specificity, which corresponds to the false-positive rate. The two curves are labeled \"VHI -\" and \"VHI +\", corresponding to different categories or conditions, where \"VHI -\" denotes cases in which VHI scores did not improve following voice therapy, while \"VHI +\" indicates cases in which VHI scores showed improvement post-therapy. The curves in Figure 6 demonstrate that the model exhibits high sensitivity and specificity, as they are positioned above the diagonal line, indicating a strong ability to \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Reference**} \\ \hline  & & **VHI (–)** & **VHI (+)** & **Total** \\ \hline \multirow{3}{*}{Predicted} & VHI (–) & 0 & 3 & 3 \\  & VHI (+) & 1 & 22 & 23 \\ \cline{1-1}  & Total & 1 & 25 & 26 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrices of RF model. \begin{table}
\begin{tabular}{c c} \hline \hline  & Gender, Smoking Status, Alcohol \\ Input Layer & Input Factors & Consumption, Voice User Status, Coffee \\  & & Consumption, Comorbidity, Surgery \\  & & Status, and Voice Training Status \\  & Number of Units & 9 (Including Bias) \\ \hline \multirow{3}{*}{Hidden layer} & Number of hidden layers & 1 \\  & Number of units & 2 \\ \cline{1-1}  & Activation function & Hyperbolic tangent \\ \hline \multirow{3}{*}{Output layer} & Dependent variable & VHI \\ \cline{1-1}  & Number of units & 1 \\ \cline{1-1}  & Activation function & Softmax \\ \cline{1-1}  & Error function & Cross-entropy \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Information related to MLP modeling and results. \begin{table}
\begin{tabular}{c c} \hline \hline  & Gender, Smoking Status, Alcohol \\ Input Layer & Input Factors & Consumption, Voice User Status, Coffee \\  & & Consumption, Comorbidity, Surgery \\  & & Status, and Voice Training Status \\  & Number of Units & 9 (Including Bias) \\ \hline \multirow{3}{*}{Hidden layer} & Number of hidden layers & 1 \\  & Number of units & 2 \\ \cline{1-1}  & Activation function & Hyperbolic tangent \\ \hline \multirow{3}{*}{Output layer} & Dependent variable & VHI \\ \cline{1-1}  & Number of units & 1 \\ \cline{1-1}  & Activation function & Softmax \\ \cline{1-1}  & Error function & Cross-entropy \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification performance of RF model. distinguish between positive and negative outcomes. The AUC-ROC value was 0.871; overall, this ROC curve suggests that the classification model for the VHI performs effectively, with high sensitivity and specificity.