Exploring of Classification Methods for Early Detection of Alzheimer’s Disease
G Stalin Babu1, S N Tirumala Rao2, R Rajeswara Rao3

1G Stalin Babu, Dept. of CSE, Aditya Institute of Technology and Management, Tekkali, A.P., India.
2S N Tirumala Rao, Dept. of CSE, Narasaraopeta Engg College. Narasaraopeta , A.P., India.
3R Rajeswara Rao, Dept. of CSE,JNTUK University College of Engineering, Vizianagaram, A.P., India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 5206-5211 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8215088619/2019©BEIESP | DOI: 10.35940/ijeat.F8215.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Alzheimer’s disease (AD) is a degenerative brain disease, a common health problem in elderly pesople which causes decline in memory and affected on nerve cells. AD has different stages like mild congestive impairment (MIC) (early stage), moderate (middle stage), severe (late stage) it is essential to detect AD early in MIC, so that pre-emptive measures can be taken. Significant research was carried out over the past century to diagnose and detect this disease early. The objective of the article is provide a review evaluation and critical analysis of the recent research work done to early diagnosis of AD using Machine Learning Strategies.
Keywords: Alzheimer disease, Classification, Machine Learning