Alzheimer’s disease (AD) is one of the most common neurodegenerative
diseases (dementia) among the aged population. In this paper, we propose a unique
machine learning-based framework to discriminate subjects with the first classification
of AD. The training data, preprocessing, feature selection, and classifiers all affect the
output of machine-learning-based methods for AD classification. This chapter
discusses a new comprehensive scheme called Progression Prediction and
Classification of Alzheimer’s Disease using MRI (PPC-AD-MRI). Considering the
data gathered with T1-weighted MRI clinical OASIS progressive information, the
consequences have been evaluated in terms of precision, recall, F1 score, and accuracy.
This recommended model with enhanced accuracy confirms its suitability for use in
AD classification. Other methods can also be used successfully in the disease’s early
detection and diagnosis in medicine and healthcare.
Keywords: Confusion metrics, OASIS dataset, Random Forest Classifier, SGD Gradient Classifier.
