Supervised Machine Learning Algorithms For Early Diagnosis Of Alzheimer’s Disease
Prasad B S1, Akhilaa2

1Prasad B S, Department of ISE, CMR Institute of Technology, Bangalore, India. Email:
2Akhilaa, Department of ISE, CMR Institute of Technology, Bangalore, India. 

Manuscript received on 09 August 2019. | Revised Manuscript received on 15 August 2019. | Manuscript published on 30 September 2019. | PP: 7964-7967 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6646098319/19©BEIESP | DOI: 10.35940/ijrte.C6646.098319

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Abstract: Alzheimer’s is a neurodegenerative disease which can eventually leads to dementia. Mostly occurring in elderly people over the age of 65, it is hard to detect and diagnose correctly. Most common symptoms include memory loss and slow deterioration of cognitive functions. Given that these symptoms are seen often in old people, this hinders the detection of Alzheimer’s disease (AD). Alzheimer’s is currently incurable, but detection of the disease during its early stage is often beneficial to the patient, since there are treatments which can considerably improve the quality of life of the patient. However this can only be done if the patient has been diagnosed at a stage before any permanent brain damage has been done. Most of the current methods for detecting and diagnosing AD are not good enough. It is the need of the hour to develop better and early diagnostic tools. With the improvements in the field of machine learning, we now have the tools needed to drastically improve detection of Alzheimer’s. We examine various machine learning methods and algorithms to find a method which can boost the chances of detecting the disease. We will use the following algorithms: Decision Tree, SVM, Random Forest and Adaboost. The dataset being used is the longitudinal MRI data available included in the OASIS dataset. We will use the aforementioned algorithms on the dataset and compare the accuracies achieved to find an optimal.
Keywords: Adaboost, Alzheimer’s Disease, Decision Tree, Machine Learning, Random forest, Supervised Learning.

Scope of the Article: Machine Learning