## 4 Discussion  
This study aimed to analyze radiomic features of the parotid glands with reduced shape effect in US images that contribute towards the differentiation of age and sex. This differentiation aims to establish a normal reference for the radiomic diagnosis of parenchymal disease of the parotid gland. Through feature selection from different ROI shapes, discretized intensity skewness and kurtosis, intensity skewness, and GLCM angular second moment emerged as the most significant features for overall classification. The best performance in both age group and sex classifications was achieved with a perfect AUC (1.000) by using a combination of IGR or SGD and NB. Similarly, a perfect level of performance for concurrently classifying both age group and sex was achieved using the combination of SGD and RF. Discretized intensity skewness or kurtosis measures the skewness (measure of asymmetry) or the kurtosis (measure of tailedness) of a histogram after discretizing (normalizing) the image; in the present study, a gray level of 256 was discretized to 10 bins, resulting in a bin size of 25.6. On the other hand, intensity skewness measures the skewness of a histogram before image normalization. Discretized intensity skewness was the most significant attribute selected by IGR in both age group and sex classification, and when combined with the NB model, it produced a perfect result. Additionally, discretized intensity kurtosis and GLCM angular second moment frequently contributed to classifying age group and sex using various algorithms, although they were not the top-ranked attributes. GLCM angular second moment evaluates the uniformity in the gray level distribution in an image. Therefore, the skewness of the discretized histogram can be inferred to vary most substantially among all subgroups, while the kurtosis of the discretized histogram and the gray level distribution remain consistently dissimilar, according to the classification models. The combinations of IGR and NB, and SGD and GB or NB, performed perfectly in discriminating age groups, whereas sex classification achieved excellent performance with additional combinations such as IGR, LASSO, R, and SGD paired with NB, KNN, or both. This difference can be explained by several factors. First, age groups are a fabricated concept and may not be as biologically disparate from each other as sex. Moreover, the mechanism and strengths of the feature selection algorithms and classification models are divergent: IGR, derived from decision tree learning, involves the length and number of tree branches to select an attribute, in other words, the ratio of information gain, favoring attributes with fewer significant values. SGD stochastically approximates the gradient descent, replacing the actual gradient, which reduces computational burden in high-dimensional optimization tasks. Meanwhile, the NB classifier, a probabilistic classifier, is based on a principle where features' values are considered independent from one another. In the present study, the radiomic features were inherently independent from one another, and the features that were handpicked by IGR or SGD for age group differentiation were significant. This may have led to IGR or SGD with NB achieving a perfect classification. The statistical algorithms for feature selection also included LASSO and R. LASSO is a type of regression analysis model that assumes only a few of the coefficients in the linear model are non-zero. This algorithm is used for both variable selection and regularization to enhance prediction accuracy. R is an updated version of the relief algorithm. Originally, relief selects features that are significantly sensitive to feature interactions by filter-method approach. R calculates near-hit and near-miss distances among features using the Manhattan norm and not the Euclidean norm. Other machine learning models, in addition to NB, included RF, GB, and KNN.