Machine learning algorithms for the diagnosis of Alzheimer and Parkinson disease

Abstract

Dementia is a general term used to indicate any disorder related to human memory. The various memory-related problems severely affect the human brain and so the individual feels difficulty in doing their normal physical as well as mental activities. There are different types of dementia that exist, but the commonly seen and fatal types of dementia are Alzheimer’s disease (AD) and Parkinson’s disease (PD). In this paper different efficient Machine Learning Techniques are selected analysed their behaviours in the diagnosis of AD and PD using Positron Emission Tomography (PET). The PET image dataset used in this work consists of 1050 images with AD, PD and Healthy Brain images. The total number of images is split into two different categories in the ratio of 7:3 for training and testing respectively. The different machine learning classifiers used are Bagged Ensemble, ID3, Naive Bayes and Multiclass Support Vector Machine. The classification of the AD and PD with the reference of a healthy brain is done by comparing the input image with the trained samples in the PET image database. In the comparison of trained samples with the input image for the PET images, the bagged ensemble learning classifier worked better than the other classification algorithms and yielded an accuracy of 90.3%.

Keywords:

• Alzheimer’s disease (AD)
• bagged ensemble
• decision tree
• dementia
• machine learning techniques
• naive bayes
• Parkinson’s disease (PD)
• positron emission tomography (PET)
• support vector machine (SVM)

Disclosure statement

No potential conflict of interest was reported by the author(s).