Comparative study on machine learning models for early diagnose of Alzheimer's Disease: Multi correlation method

Abstract

Health is the most effective platform which can adopt any kind of new technologies to implement on the data produced by it. There are different brain disorders and misfunctions in which dementia is the major one. Dementia is decreasing the brain capacity to do daily works and decreases the capacity of thinking and doing some task. This article deals with the comparative study of different machine learning models to diagnose Alzheimer's Disease (AD) which provides the proof of the algorithm which is giving most accurate result in identifying the AD in advance. Machine learning and Big Data are the two pillars of the technology and everything in our life relates to those two things. In this article, a few different mechanisms are explained and why need these two pillars are necessary to protect the health care mechanisms in the real time scenario. Algorithm like Random Forest, Na�ve Bayes etc helped us to implement the comparative study on the health care concepts. In this article, early diagnose of AD is dealt with different machine learning models and in prior that data was gathered from different mechanisms and that proposed work was explained clearly.