Classification of Alzheimer’s Disease using Machine Learning Techniques

Abstract

Alzheimer’s disease (AD) is a commonly known and widespread neurodegenerative disease which causes cognitive impairment. Although in medicine and healthcare areas, it is one of the frequently studied diseases of the nervous system despite that it has no cure or any way to slow or stop its progression. However, there are different options (drug or non-drug options) that may help to treat symptoms of the AD at its different stages to improve the patient’s quality of life. As the AD progresses with time, the patients at its different stages need to be treated differently. For that purpose, the early detection and classification of the stages of the AD can be very helpful for the treatment of symptoms of the disease. On the other hand, the use of computing resources in healthcare departments is continuously increasing and it is becoming the norm to record the patient’ data electronically that was traditionally recorded on paper-based forms. This yield increased access to a large number of electronic health records (EHRs). Machine learning, and data mining techniques can be applied to these EHRs to enhance the quality and productivity of medicine and healthcare centers. In this paper, six different machine learning and data mining algorithms including k-nearest neighbors (k-NN), decision tree (DT), rule induction, Naive Bayes, generalized linear model (GLM) and deep learning algorithm are applied on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset in order to classify the five different stages of the AD and to identify the most distinguishing attribute for each stage of the AD among ADNI dataset. The results of the study revealed that the GLM can efficiently classify the stages of the AD with an accuracy of 88.24% on the test dataset. The results also revealed these techniques can be successfully used in medicine and healthcare for the early detection and diagnosis of the disease.