## 3 Results  
In the classification of age groups, two first-order and three high-order features (two GLRLM and one GLCM feature) contributed to achieving the best and excellent performance in terms of AUC (1.000) when using the combination of IGR and NB, and SGD and GB or NB. For the classification of sex, the combination of IGR and NB also yielded the best performance (AUC = 1.000), along with other combinations, including LASSO and NB or KNN, IGR and KNN, R and KNN, and SGD and NB. The contributing features comprised 3-5 first-order and 3-5 high-order features (2-4 GLCM and 1-2 GLRLM features). However, the best performance in simultaneously classifying age group and sex was observed when using the combination of SGD and RF (AUC = 1.000). The combinations mentioned above also achieved high recall and precision scores. Overall classification performance according to the combinations are depicted in Figure 4, confusion matrices are shown in Figure 5, and the details of the selected features are described in Table 3.  
Other combinations of statistical algorithm and machine learning methods that produced excellent or good results in classifying age groups are listed below in descending order in terms of AUC: IGR and KNN (AUC = 0.972), R and NB (AUC = 0.944), SGD and RF (AUC = 0.944), LASSO and KNN (AUC = 0.917), LASSO and NB (AUC = 0.889), IGR and RF (AUC = 0.889), and R and GB (AUC = 0.833). The combinations that also produced excellent or good results in sex classification were R and NB (AUC = 0.875), SGD and KNN (AUC = 0.875), LASSO and GB (AUC = 0.812), and SGD and GB (AUC = 0.812). In simultaneous classification of age groups and sex, all combinations generated either excellent or good results. Those excellent results were attained from combinations LASSO and RF (AUC = 0.978), IGR and RF (AUC = 0.978), R and RF (AUC = 0.957), LASSO and GB (AUC = 0.935), IGR and NB (AUC = 0.935), SGD and NB or KNN (AUC = 0.935), LASSO and NB (AUC = 0.913), IGR and GB (AUC = 0.913), and SGD and GB (AUC = 0.913). Good results were acquired from IGR and KNN (AUC = 0.891), R and NB (AUC = 0.891), R and GB (AUC = 0.848), LASSO and KNN (AUC = 0.826), and R and KNN (AUC = 0.804). Overall, the features that contributed to the most combinations for perfect classification were discretized intensity skewness (combinations of IGR and NB for age; IGR and NB or KNN, LASSO and NB or KNN for sex; and SGD and RF for both) and kurtosis (combinations of IGR and NB, SGD and GB or NB for age; LASSO and NB or KNN, SGD and NB for sex; and SGD and RF for both), and GLCM angular second moment (combinations of IGR and NB for age and all combinations for sex). Discretized intensity skewness was the best-ranked attribute by IGR among other selected attributes for age and sex classification. For the various combinations that achieved perfect classifications of sex, intensity skewness and GLCM angular second moment were the most frequently included features. Figure 4: Performance metrics for four statistical algorithms and four classification models: (**a-c**) area under the receiver operating characteristic curve results, (**d–f**) recall, and (**g-i**) precision. Metrics are presented for (**a,d,g**) age, (**b,e,h**) sex, and (**c,f,i**) combined age and sex classification. The highest scores are highlighted in bold. AUC, area under the receiver operating characteristic curve; LASSO, least absolute shrinkage and selection operator; IGR, information gain ratio; SGD, stochastic gradient descent; KNN, k-nearest neighbors. Figure 5: Confusion matrices showing the classification results for each combination of statistical algorithm and machine learning model: (**a**) age, (**b**) sex, and (**c**) combined age and sex classification.