Journal article Open Access

Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer's disease

Sai Sindhuri Nasina; A. Rama Mohan Reddy

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    <subfield code="a">Alzheimer's disease, Dementia, Deep learning, Machine learning, Artificial neural networks, Ensemble methods.</subfield>
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    <subfield code="u">Ph.D., Computer Science and Engineering, Sri  Venkateswara University College of Engineering, Tirupati.</subfield>
    <subfield code="a">A. Rama Mohan Reddy</subfield>
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    <subfield code="a">Blue Eyes Intelligence Engineering  &amp; Sciences Publication(BEIESP)</subfield>
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    <subfield code="u">Ph.D., Computer Science and Engineering, Sri  Venkateswara University College of Engineering, Tirupati.</subfield>
    <subfield code="a">Sai Sindhuri Nasina</subfield>
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    <subfield code="a">Efficient Machine Learning Techniques to  Diagnose and Predict Alzheimer's disease</subfield>
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    <subfield code="a">&lt;p&gt;Recent research in computational engineering have evidenced the design and development numerous intelligent models to analyze medical data and derive inferences related to early diagnosis and prediction of disease severity. In this context, prediction and diagnosis of fatal neurodegenerative diseases that comes under the class of dementia from medical image data is considered as the challenging area of research for many researchers. Recently Alzheimer&amp;rsquo;s disease is considered as major category of dementia that affects major population. Despite of the development of numerous machine learning models for early diagnosis of Alzheimer&amp;rsquo;s disease, it is observed that there is a lot more scope of research. Addressing the same, this article presents a systematic literature review of machine learning techniques developed for early diagnosis of Alzheimer&amp;rsquo;s disease. Furthermore this article includes major categories of machine learning algorithms that include artificial neural networks, Support vector machines and Deep learning based ensemble models that helps the budding researchers to explore the scope of research in predicting Alzheimer&amp;rsquo;s disease. Implementation results depict the comparative analysis of state of art machine learning mechanisms.&lt;/p&gt;</subfield>
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