Table 3.

Classification results on dataset 1 (AUC/accuracy/sensitivity/specificity)

Features	Linear discriminant analysis	Support vector machine
A: Planar measurement	0.870/0.811/0.678/0.829: {1, 2}	0.860/0.798/0.668/0.815: {3}
B: 3D shape	0.954/0.919/0.941/0.916: {4, 5}	0.952/0.920/0.927/0.919: {4, 5}
C: Intensity	0.977/0.887/0.942/0.879: {9, 10, 11}	0.964/0.898/0.840/0.906: {9, 10}
A + C	0.976/0.883/0.937/0.876: {9, 10, 11}	0.964/0.919/0.906/0.922: {1, 2, 7, 10}
B + C	0.984/0.945/0.969/0.942: {4, 5, 9, 10, 11}	0.978/0.940/0.879/0.949: {4, 5, 7, 10}
A+B+C	0.984/0.942/0.975/0.937: {2, 4, 5, 9, 10, 11}	0.977/0.936/0.911/0.941: {1, 5, 6, 9, 10}

Selected features are noted in the braces (feature index corresponds to table 2), the best results are highlighted in bold.

AUC, area under the curve; 3D, three dimensional.