Abstract
Alzheimer’s disease is a neural disorder which the cause for progressive irreversible neurologic disorder. It is difficult to detect in primary stages and yet the modalities for the progression which are complex which are not completely known. The way of detection of Alzheimer’s disease is through neuro imaging technique by the diffusion of the resonance caused by magnetism represented as MR. In order to understand the real and root causes there is a need of studying huge amount of MR images. So as to resolve the issues of time constraint and faster access to the features of MR image, machine learning techniques are needed, in order to process large quantities of medical data. In machine learning set of rules determine the output based on the goal. The study of the mild cognitive impairment according to the classification problem is done as well as the diffusion of the data along with the other sources. The systematic review of several predictive learning methods is presented with reference to their work and the score of their performance. From the research work, machine learning proves to be an efficient way for categorizing and classification of the Alzheimer’s disease with the high accuracy.
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Dutta, P., Mishra, S. (2022). A Comprehensive Review Analysis of Alzheimer’s Disorder Using Machine Learning Approach. In: Mishra, S., Tripathy, H.K., Mallick, P., Shaalan, K. (eds) Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. Studies in Computational Intelligence, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-19-1076-0_4
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