## 2 Materials and Methods
### Radionic Analysis
#### 2.2.2 Dimension Reduction and Classification  
First, features with an ICC value > 0.80 between two different ROI shapes were selected for reproducibility and redundancy. A total of 38 features, consisting of 19 first-order and 19 high-order features were selected (Table 2).  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**First-Order Features**} & \multicolumn{1}{c}{**High-Order Features**} \\ \hline Intensity-based features & Gray-level co-occurrence matrix (GLCM) \\ Mean intensity & GLCM Joint variance \\ Intensity skewness & GLCM Joint entropy (based log2) \\ Intensity kurtosis & GLCM Joint entropy (based log10) \\ Minimum intensity & GLCM Angular second moment \\ Maximum intensity & GLCM Dissimilarity \\
25 th intensity percentile & GLCM Inverse difference \\
50 th intensity percentile & GLCM Correlation \\
75 th intensity percentile & Gray-level run length matrix (GLRLM) \\ Standard deviation & GLRM Short runs emphasis \\ Intensity histogram features & GLRLM Long runs emphasis \\ Mean discretized intensity & GLRLM Low gray level run emphasis \\ Discretized intensity skewness & GLRLM High gray level run emphasis \\ Discretized intensity kurtosis & GLRLM Short run low gray level emphasis \\
25 th discretized intensity percentile & GLRLM Short run high gray level emphasis \\
50 th discretized intensity percentile & GLRLM Long run low gray level emphasis \\
75 th discretized intensity percentile & GLRLM Run percentage \\ Intensity histogram standard deviation & Gray-level size zone matrix (GLSZM) \\ Discretized intensity entropy (based log10) & GLSZM Low gray level zone emphasis \\ Discretized intensity entropy (based log2) & GLSZM High gray level zone emphasis \\ Discretized intensity uniformity & GLSZM Small zone low gray level emphasis \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radionic features chosen after evaluating the intraclass correlation coefficient.  
Figure 3: Examples of regions of interest selection in an ultrasound image of a parotid gland: (**a**) polygonal, (**b**) square shape.  
Subsequently, four statistical algorithms or machine learning methods were applied to select features according to their contribution towards accurate classification of the sex and age group: least absolute shrinkage and selection operator (LASSO), information gain ratio (IGR), relief (R), and stochastic gradient descent (SGD). The selected features were then used to classify the sex, age groups, or sex and age concurrently, using four machine learning models, random forest (RF), gradient boosting (GB), naive Bayes (NB), and k-nearest neighbor (KNN).  
Classification performance was measured using the area under the receiver operating characteristic curve (AUC), recall, and precision. The results were interpreted as follows: \(0.5\leq\text{AUC}<0.6\), fail; \(0.6\leq\text{AUC}<0.7\), poor; \(0.7\leq\text{AUC}<0.8\), fair; \(0.8\leq\text{AUC}<0.9\), good; and \(\text{AUC}\geq 0.9\), excellent [28].  
ICC calculation was performed using IBM SPSS Statistics for Windows, Version 29 (IBM Corp., Armonk, NY, USA). Further dimension reduction and classification model development were performed using Orange software (Orange, Ljubljana, Slovenia, available from [[https://orangedatamining.com/](https://orangedatamining.com/)]([https://orangedatamining.com/](https://orangedatamining.com/))) [29].