Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification

Cognitive impairment must be diagnosed in Alzheimer’s disease as early as possible. Early diagnosis allows the person to receive effective treatment benefits apart from helping him or her to remain independent longer. In this paper, different feature selection techniques are utilized with different classifiers in the classification of this chronic disease as normal control (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) based on the MRI images of ADNI dataset. Dimensionality reduction plays a major role in improving classification performance when there are fewer records with high dimensions. After different trials to select the ample features, support vector machine (SVM) with radial basis function kernel is found to produce better results with 96.82%, 89.39% and 90.40% accuracy for binary classification of NC/AD, NC/MCI and MCI/AD, respectively, with repeated tenfold stratified cross-validation. Combining mini-mental state examination (MMSE) score to the MRI data, there has been an improvement of 2.7% in the MCI/AD classification, but it does not have much influence in the NC/AD and NC/MCI classification.