FIGURE 2.

Model selection of different classes of machine learning models. (a) Several classes of models were tested to estimate physiological age, defined as the chronological age predicted by the model. An optimization of the hyperparameters of each model was performed on the training dataset, and the final achieved performance tested on the training and test datasets (coefficient of determination R ² and mean absolute error MAE). MultiLayer Perceptron MLP and XGBoost model achieved the best performances. (b) Graphical representation of the predicted physiological age defined using MLP, XGBoost and XGBoost with Custom loss as function of chronological age. The red line highlights situations where physiological age is identical to chronological age. Custom loss applied to XGBoost improved XGBoost, by moderating the performance discrepancy across the age groups.