Prediction and Classification of Alzheimer’s Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers - Gupta, Yubraj; Lama, Ramesh Kumar; Kwon, Goo-Rak; , Alzheimer's Disease Neuroimaging Initiative; Weiner, Michael W.; Aisen, Paul; Weiner, Michael; Aisen, Paul; Petersen, Ronald; Jack, Clifford R.; Jagust, William; Trojanowki, John Q.; et al. (Frontiers in Computational Neuroscience, 2019)
Alzheimer’s disease, including its MCI phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: FDG-PET, sMRI, CSF protein levels, and APOE genotype. The ADNI baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the HC group. For each image, we extracted 246 regions of interest using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass SVM classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used the truncated SVD dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. Our combined method achieved an AUC-ROC of 98.33%, 93.59%, 96.83%, 94.64%, 96.43%, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches.