Combining multiple approaches for the early diagnosis of Alzheimer's Disease

One of the current challenges in Alzheimer's Disease (AD)-related research is to achieve an early and definite diagnosis. Automatic classification of AD is typically based on the use of feature vectors of high dimensionality, containing few training patterns, which leads to the curse-of-dimensionality problem. It is indispensable to find good approaches for selecting a subset of the original set of features. In this work, a method to perform early diagnosis of AD is proposed, combining different feature reduction approaches on both brain MRI studies and expression values of blood plasma proteins. Each selected set of features is used to train a Support Vector Machine (SVM), then the set of SVM is combined by weighted sum rule. Moreover, a novel approach for considering the feature vector as an image is proposed, different texture descriptors are extracted from the image and used to train a SVM. The superior performance of the proposed system is obtained without any ad hoc parameter optimization (i.e., the same ensemble of classifiers and the same parameter settings are used in all datasets). The MATLAB code for the ensemble of classifiers will be publicly available3 to other researchers for future comparisons.