Table 7.

Model learning results based on disease diagnostic and prescription data.

Age range (years)	Classifier	Parameters	Accuracy	Precision	Recall	F1 score
Up to 1	Logistic regression	C=0.1	0.732	0.691	0.732	0.688
Up to 2	Gradient boosting	learning rate: 0.4; n estimators: 4	0.767	0.743	0.767	0.738
Up to 3	Random forest	n estimators: 128	0.802	0.800	0.802	0.783
Up to 4	Random forest	n estimators: 128	0.832	0.819	0.832	0.816
Up to 5	Random forest	n estimators: 32	0.835	0.813	0.835	0.817
Up to 6	Gradient boosting	learning rate: 0.4; n estimators: 4	0.858	0.850	0.858	0.853
Up to 7	Random forest	n estimators: 32	0.849	0.830	0.849	0.834
Up to 8	Random forest	n estimators: 128	0.866	0.848	0.866	0.854
Up to 9	Gradient boosting	learning rate: 0.4; n estimators: 4	0.857	0.859	0.857	0.857
Up to 10	Random forest	n estimators: 128	0.898	0.878	0.898	0.885
Up to 11	Random forest	n estimators: 64	0.914	0.916	0.914	0.905
Up to 12	Gradient boosting	learning rate: 0.4; n estimators: 1	0.832	0.833	0.832	0.829
Up to 13	Gradient boosting	learning rate: 1.0; n estimators: 1	0.891	0.896	0.891	0.893