Toward Alzheimer’s disease classification through machine learning

Alzheimer’s disease (AD) and cognitive impairment due to aging are the recently prevailing diseases among aged inhabitants due to an increase in the aging population. Several demographic characters, structural and functional neuroimaging investigations, cardio-vascular studies, neuropsychiatric symptoms, cognitive performances, and biomarkers in cerebrospinal fluids are the various predictors for AD. These input features can be considered for the prediction of symptoms whether they belong to AD or normal cognitive impairment due to aging. In the proposed study, the hypothesis is derived for supervised learning methods such as multivariate linear regression, logistic regression, and SVM. Feature scaling and normalization are performed with features as initial steps for applying the parameters to derive the hypothesis. Performance metrics are analyzed with the implementation results. The present work is applied to 1000 baseline assessment data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) studies that give conversion prediction. The comparison of results in the literature suggests that the efficiency of the proposed study is highly advantageous in differentiating AD pathology from cognitive impairment due to aging.