Table 11.

XGBoost accuracies (mean ± standard deviation in %) for the external AIBL data set

Exclusion method	Number of training subjects excluded
(base model)	0	50	100	150	200	250
Random (-)	60.00 ±5.00	55.00 ±3.98	57.50 ±4.06	55.71 ±2.37	52.14 ±5.35	51.07 ±4.80
LOO (LR)	60.00 ±5.00	58.93 ±3.29	61.79 ±4.53	63.21 ±3.93	58.21 ±3.93	59.64 ±4.80
LOO (RF)	60.00 ±5.00	59.64 ±5.99	61.43 ±5.71	61.79 ±2.79	61.07 ±4.64	56.07 ±4.53
Data Shapley (LR)	60.00 ±5.00	61.07 ±3.73	61.43 ±3.85	62.14 ±3.98	63.21 ±5.31	61.07 ±1.92
Data Shapley (RF)	60.00 ±5.00	64.64 ±5.64	62.86 ±2.86	62.86 ±1.75	61.79 ±2.29	58.57 ±2.37

Different methods were used to identify and focus on the training subjects with the most informative data. Ten repetitions with different seeds were performed for every exclusion data set. The best results are highlighted in bold