Computer Methods and Programs in Biomedical Signal and Image Processing

was used to assess the performance and repeated 10 times to reduce the possible bias. A least absolute shrinkage and selection operator (LASSO) was used for feature selection. LASSO parameter λ retained for the experiments was obtained by nested cross-validation on the training dataset. Tables 3 and 4 show the obtained results for AD vs. NC and MCI vs. NC when using each feature, respectively. As can be seen, the first-level and second-level features outperformed the third-level feature for AD and MCI diagnosis. This could be explained since the two first-level features are more linked to VOI's property or connectivity between each pair of VOIs, while the third-level feature represents an overall connectivity between a VOI and the others.

This second proposed method was compared with the state-of-the-art methods, including Hinrichs's method [14], Gray's method [12], Li's method [36], and Padilla's method [21], which were applied to FDG-PET data. The results are shown


## Table 3.

Performance of different types of feature for AD vs. NC (%).


#### Table 4.

Performance of different types of feature for MCI vs. NC (%).


#### Table 5.

Performance comparison for AD vs. NC (%).

Alzheimer's Disease Computer-Aided Diagnosis on Positron Emission Tomography Brain Images… DOI: http://dx.doi.org/10.5772/intechopen.86114


#### Table 6.

Performance comparison for MCI vs. NC (%).

in Tables 5 and 6. The proposed approach outperformed these methods in terms of ACC and SEN for AD diagnosis. For MCI diagnosis, our method outperforms the other methods in SEN and AUC, and the difference with the best result is 0.21 and 1.65% for ACC and SPE, respectively. Moreover, compared with Tables 3 and 4, the significant improvements indicate the effectiveness of the ensemble classification, thereby explaining the multilevel features are necessary.
