CLASSIFICATION AND TREATMENT OF DIFFERENT STAGES OF ALZHEIMER’S DISEASE USING VARIOUS MACHINE LEARNING METHODS

Sandhya Joshi¹*, Vibhudendra Simha G.G.², Deepa Shenoy P.³, Venugopal K.R.⁴, Patnaik L.M.^5
1Research Scholar, Department of Computer Science and Engineering, M G R University, Chennai, India
²Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, K R Circle, Bangalore-01, India
³Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, K R Circle, Bangalore-01, India
⁴Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, K R Circle, Bangalore-01, India
⁵Vice Chancellor, Defence Institute of Advanced Technology, Pune, India
* Corresponding Author : sanjoshi17@yahoo.com

Received : -     Accepted : -     Published : 15-06-2010
Volume : 2     Issue : 1       Pages : 44 - 52
Int J Bioinformatics Res 2.1 (2010):44-52
DOI : http://dx.doi.org/10.9735/0975-3087.2.1.44-52

Keywords : Alzheimer’s Disease, Neural Networks, Multilayer Perceptron, Bagging, Decision tree, CANFIS and Genetic Algorithms, Pharmacotherapeutic intervention, Non-Pharmacotherapeutic intervention
Conflict of Interest : None declared

There has been a steady rise in the number of patients suffering from Alzheimer’s disease (AD) all over the world. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. The patient’s records are collected from National Institute on Aging, USA. The Sample consisted of initial visits of 496 subjects seen either as control or as patients. Patients were concerned about their memory at the National Institute on Aging. It also consisted of patients and caregiver interviews. This research work presents different models for the classification of different stages of Alzheimer’s disease using various machine learning methods such as Neural Networks, Multilayer Perceptron, Bagging, Decision tree, CANFIS and Genetic algorithms. The classification accuracy for CANFIS was found to be 99.55% which was found to be better when compared to other classification methods. Based on the outcome of classification accuracies, various management and treatment strategies such as pharmacotherapeutic and non pharmacotherapeutic interventions for mild, moderate and severe AD were elucidated, which can be of enormous use for the medical professionals in diagnosis and treatment of AD.

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