## 3 Results
### Evaluation of the Performances of the Computational Models  
The LASSO regularization, iterated 100 times, identified the following as the most frequently selected features: age_FE, d_LA, Hr, LV-mass/BSA, PW, and WBr. H/WBr was excluded by the LASSO regularization process as it was highly correlated (R\({}^{2}\) > 0.9) with Hr. The best lambda value identified in the cross-validation step, after the 100 iterations, was 6.34 \(\times\) 10\({}^{-2}\) (Figure 5). Figure 4: Boxplots of H/WBr (**a**), Hr (**b**), and WBr (**c**) for “Wild-type” versus “Hereditary” etiology. [PERSON]’s test \(p\)-values obtained to compare groups are reported. Figure 3: Bland–Altman plot of H/WBr with the GM versus the CM calculated in the MIM-assisted workflow. The black line and the red lines represent the mean difference (that is, the bias) and the 95% CI, respectively. The variables that were characterized by a non-zero coefficient after the LASSO regularization at the best lambda value were the patient age at first examination and the Hr. The model developed a high AUC of 88.0% [CI 80.6-95.5%], 89.6% [CI 77.0-100%], and 87.3% [CI 74.3-100%] for the training, test, and validation sets, respectively. The best cut-off obtained a specificity of 90% and a sensitivity of 84% on the test dataset and 66.7% and 95.7% on the validation dataset. Similarly, a specificity of 88% and a sensitivity of 60% on the test dataset and 78.3% and 33.3% on the validation dataset were found for the RF approach. The AUC of the RF approach was significantly lower compared to LASSO, ascribed to the triangular shape of the ROC curve, which was obtained from a single point in addition to the 0-100% extremities. The NN model (reported in Supplementary Material Figure S1) had good performance with high AUC values of 92.3% [CI 84.7-99.9%], 84.4% [CI 70.4-98.4%], and 78.3% [CI 61.1-95.4%] for the ROC curve on the training, test, and validation sets. All ROC curves are reported in Figure 6.  
The %IncMSE and the IncNodePurity for each variable inserted in the RF classifier are shown in Figure 7. The variables age_FE and Hr had a greater impact with values equal to 25.4 and 14.7, respectively, and therefore contribute to making the model more accurate. Figure 5: (**a**) LASSO-CV to find the best lambda value that minimizes the mean squared error. The identified best lambda value was 6.34 \(\times\) 10\({}^{-2}\). (**b**) The progressive shrinkage of the LASSO model to select the optimal variables with non-zero coefficients in correspondence with the best value of lambda (dotted red line). Figure 6: ROC curves of the LASSO regression model, Random Forest model, and Neural Network model obtained on the training, test, and validation sets. The AUCs of the three ROC curves are reported. The IncNodePurity also showed higher values for the clinical variable, age_FE, and the MN variable, Hr, with values equal to 6.45 and 4.45, respectively. Table 3 reported the values of the intercept and non-zero coefficients associated with the variables in correspondence with the best lambda for the LASSO model, while the importance of each variable within the RF model was indicated by the parameters %IncMSE and IncNodePurity, and the relative importance of clinical and NM variables in the Neural Network model was estimated by [PERSON]'s algorithm. The confusion matrices, shown in Figure 8, describe the performance of the three models on the external validation dataset, highlighting the true positives and negatives (green boxes) from the misclassifications (red boxes).