TABLE 7.

Morphology features: performance and hyperparameters of ML models.

Model	Train_Acc	Val_Acc	Test_Acc	Train_F1	Val_F1	Test_F1	Hyperparameters
XGBoost	88.88	86.14	86.8	0.853	0.8174	0.829	gamma=0.1, learning_rate=0.2, max_depth=5, estimators=100, reg_alpha=0.2, reg_lambda=0.3
Random Forest	85.29	82.99	83.58	0.8471	0.7786	0.7901	min_samples_leaf=1, min_samples_split=6, estimators=400
LightGBM	87.15	85.73	86.29	0.831	0.812	0.822	lr=0.1, max_depth: −1, min_child_samples: 20, num_leaves=31
Extra Trees	86.66	82.51	83.36	0.817	0.758	0.773	criterion=gini, min_samples_leaf=10, min_samples_split=5, estimators=100