Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems

Progression Prediction and Classification of Alzheimer’s Disease using MRI

Author(s): Sruthi Mohan* and S. Naganandhini

Pp: 181-196 (16)

DOI: 10.2174/9781681089553122010014

* (Excluding Mailing and Handling)


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.

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