Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer’s disease
Sai Sindhuri Nasina1, A. Rama Mohan Reddy2

1Sai Sindhuri Nasina*, Ph.D., Computer Science and Engineering, Sri Venkateswara University College of Engineering, Tirupati, India.
2A. Rama Mohan Reddy, Professor, Computer Science and Engineering, S V University College of Engineering, Tirupati, India.
Manuscript received on January 23, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 29, 2020. | PP: 3953-3960 | Volume-9 Issue-3, February 2020. | Retrieval Number:  C6508029320/2020©BEIESP | DOI: 10.35940/ijeat.C6508.029320
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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: 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’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’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’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’s disease. Implementation results depict the comparative analysis of state of art machine learning mechanisms.
Keywords: Alzheimer’s disease, Dementia, Deep learning, Machine learning, Artificial neural networks, Ensemble methods.