. Author manuscript; available in PMC: 2013 Mar 1.

Published in final edited form as: NMR Biomed. 2012 Jan 29;25(3):476–488. doi: 10.1002/nbm.1804

Table 4.

Classification accuracy of immature and mature constructs using univariate analysis and support vector machine multivariate analysis.

	Training	Validation
T₁	0.858 ± 0.046	0.836 ± 0.069
T₂	0.838 ± 0.042	0.819 ± 0.072
k_m	0.862 ± 0.039	0.850 ± 0.074
ADC	0.787 ± 0.062	0.758 ± 0.081
(T₁, T₂)	0.962 ± 0.032	0.913 ± 0.065
(T₁,k_m)	0.998 ± 0.009	0.959 ± 0.053
(T₁, ADC)	0.940 ± 0.029	0.893 ± 0.060
(T₂,k_m)	1.000 ± 0.000	0.971 ± 0.037
(T₂, ADC)	1.000 ± 0.000	0.878 ± 0.073
(k_m, ADC)	1.000 ± 0.000	0.978 ± 0.042
(T₁, T₂, km)	0.981 ± 0.016	0.948 ± 0.051
(T₁, T₂, ADC)	0.966 ± 0.024	0.921 ± 0.057
(T₁, k_m, ADC)	1.000 ± 0.000	0.958 ± 0.056
(T₂, k_m, ADC)	1.000 ± 0.000	0.936 ± 0.058
(T₁, T₂, k_m, ADC)	0.980 ± 0.019	0.948 ± 0.044

Samples (N = 48) were randomly split into a training set (n = 36) and a validation set (n = 12) for each classification iteration. Accuracy is reported as the average from 100 iterations.