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A Comparative Analysis to Visualize the Behavior of Different Machine Learning Algorithms for Normalized and Un-Normalized Data in Predicting Alzheimer’s Disease

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Advancement in technology has helped people to live a long and better life. But the increased life expectancy has also elevated the risk of age related disorders, especially the neurodegenerative disorders. Alzheimer’s is one such neurodegenerative disorder, which is also the leading contributor towards dementia in elderly people. Despite of extensive research in this field, scientists have failed to find a cure for the disease till date. This makes early diagnosis of Alzheimer’s very crucial so as to delay its progression and improve the condition of the patient. Various techniques are being employed for diagnosing Alzheimer’s which include neuropsychological tests, medical imaging, blood based biomarkers, etc. Apart from this, various machine learning algorithms have been employed so far to diagnose Alzheimer’s in its early stages. In the current research, authors compared the performance of various machine learning techniques i.e., Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF) and Multi Layer Perceptron (MLP) on Alzheimer’s dataset. This paper experimentally demonstrated that normalization exhibits a predominant role in enhancing the efficiency of some machine learning algorithms. Therefore it becomes imperative to choose the algorithms as per the available data. In this paper, the efficiency of the given machine learning methods was compared in terms of accuracy and f1-score. Naïve Bayes gave a better overall performance for both accuracy and f1-score and it also remained unaffected with the normalization of data along with LDA, DT and RF. Whereas KNN, SVM and MLP showed a drastic (17% to 86%) improvement in the performance when they are given normalized data as compared to un-normalized data from Alzheimer’s dataset.
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Keywords: Alzheimer’s; Dementia; Machine Learning; Neurodegeneration; Normalization

Document Type: Research Article

Affiliations: 1: Department of Computer Science & IT, University of Jammu, 180006, Jammu, India 2: Department of Computer Science & IT, Bhaderwah Campus, University of Jammu, 182222, Jammu, India

Publication date: September 1, 2019

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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