For each AD key protein, using the XGBoost classification and regression algorithm , three different ML models were built and then evaluated based on balanced accuracy, precision and F1 score metrics, respectively. Balanced Accuracy is the arithmetic means of sensitivity and specificity and is used in both binary and multi-class classification ML models. It is recommended to be used when dealing with imbalanced data. Precision quantifies the number of correct positive predictions by calculating the accuracy of the true positive. F1 score was selected because it integrates precision and recall into a single metric to gain a better understanding of model performance. F1 score is commonly used as an evaluation metric in binary ML modes. These metrics are widely recommended in the literature for studies that require accurate interpretation of model effectiveness across classes .