## 4 Results
Direct transfer learning models performed below the baseline, 0.7896 \(\pm\) 0.0049 with the model transferred from the second dataset, and even lower with 0.7461 \(\pm\) 0.0027 from the third dataset. This is because these models may be biased by the source data and had fewer samples to learn from, losing generalization on the first dataset which was the largest one. However, when these models were retrained on the target data, the results of the repurposed transfer improved, and they were better than using the individual model, with values of 0.8175 \(\pm\) 0.0033 and 0.8168 \(\pm\) 0.0040 from the second and third entities, respectively. The stacking approach, which combined the results of the individual models, performed very well and had a higher AUC than the baseline at 0.8196 \(\pm\) 0.0017. However, the soft voting and federated learning approaches produced lower results, performing below the baseline. The right side of Figure 13 shows the increase or decrease in AUC for the different approaches when the target was the first dataset. All the boxes above the zero line indicate a model performing better than the baseline, which was the individual model for the first dataset (the largest). The results confirmed the previous AUC results; the performance of the repurposed models and the stacking approach were better than the baseline with very small differences in terms of \(\Delta\)AUC. More specifically, the stacking approach stood out for its capability and potential since it offered better performance than all the other approaches with an increase of 0.3% in AUC. The repurposed models also performed very well compared with the single model, with increases of 0.09% and 0.03% when data came from the second individual model and the third individual model, respectively. The  
Figure 14: (**a**) AUC and (**b**) \(\Delta\)AUC for all approaches. Target: 2 nd entity dataset. Figure 13: (**a**) AUC and (**b**) \(\Delta\)AUC for all approaches. Target: 1 st entity dataset. centralized model produced a small decrease of \(-0.55\%\). The lowest results were produced by a direct transfer from individual model 3 (the smallest) with a decrease of \(-7.04\%\), along with the voting approach and the federated approach, with decreases of 2.35% and 3.63%, respectively. With the second (medium-sized) dataset as the target, as shown on the left side of Figure 14, the results of AUC were similar to the results for the first dataset (AUC ranging between 0.8234 and 0.7821), albeit with some differences. The main difference was that the baseline (individual model trained with the second dataset) did not produce the best results, with a value of 0.8234 \(\pm\) 0.0037. The centralized model performed similarly to the reference/baseline model, confirming its potential. The model transferred directly from the first, large dataset worked well (0.8177 \(\pm\) 0.0044) but the model transferred from the third, small dataset worked worse (0.7821). The reused models performed very well, at the cost of retraining. In fact, the best results came from the repurposed first dataset. The soft-voting approach improved a little on the previous dataset with 0.8186 \(\pm\) 0.0040 and the stacking method continued to improve with 0.8257 \(\pm\) 0.0011. Finally, the federated learning approaches continued to underperform, but improved a little by now taking advantage of learning from the largest dataset, with a value of 0.8054 \(\pm\) 0.0039. Looking at the increases or decreases in AUC for the second (medium-sized) dataset, the right side of Figure 14 shows that the results were similar to the results from the first dataset, with repurposed (0.34% from the first) and stacking (0.23%) approaches giving the best results, even outperforming the individual models. The centralized model produced a very small drop of \(-0.12\%\) in AUC (lower than with the first dataset).