Data-driven meal events detection using blood glucose response patterns

Table 7 Test results for all participants^a. Resulting values of the selected model tested on the data of each of the participants

ID	Classifier	Meals	Predictions	FP	FN	TP	PPV	TPR	F\(_1\)-score	F\(_2\)-score
540	Decision Tree	20	9	8	19	1	0.11	0.05	0.07	0.06
544	Random Forest	32	38	9	3	29	0.76	0.91	0.83	0.87
552	MLP	14	18	12	8	6	0.33	0.43	0.38	0.41
559	Gaussian NB	23	31	20	12	11	0.35	0.48	0.41	0.45
563	Decision Tree	23	28	18	13	10	0.36	0.43	0.39	0.42
570	Random Forest	31	37	13	7	24	0.65	0.77	0.71	0.75
575	MLP	37	42	18	13	24	0.57	0.65	0.61	0.63
584	Decision Tree	20	34	23	9	11	0.32	0.55	0.41	0.48
588	AdaBoost	34	50	25	9	25	0.5	0.74	0.6	0.67
591	AdaBoost	37	64	38	11	26	0.41	0.7	0.51	0.61
596	Gradient Boosting	45	59	27	13	32	0.54	0.71	0.62	0.67

FP false positives, FN false negatives, TP true positives, PPV (precision) positive predictive value, TPR (recall) true positive rate
^aPatient 567 was not included due to the lack of logged meal events

ISSN: 1472-6947