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MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Disease Prediction Through Machine Learning Classifiers

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The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) (Deep-BDB 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 768))

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Abstract

Alzheimer’s disease (AD) is a neurological illness that worsens with time. The aged population has expanded in recent years, as has the prevalence of geriatric illnesses. There is no cure, but early detection and proper treatment allow sufferers to live normal lives. Furthermore, people with this disease’s immune systems steadily degenerate, resulting in a wide range of severe disorders. Neuroimaging Data from magnetic resonance imaging (MRI) is utilized to identify and detect the disease as early as possible. The data is derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) collection of 266 people with 177 structural brain MRI imaging, DTI, and PET data for intermediate disease diagnosis. When neuropsychological and cognitive data are integrated, the study found that ML can aid in the identification of preclinical Alzheimer’s disease. Our primary objective is to develop a model that is reliable, simple, and rapid for diagnosing preclinical Alzheimer’s disease. According to our findings (MRIAD), the Logistic Regression (LR) model has the best accuracy and classification prediction of about 98%. The ML model is also developed in the paper. This article profoundly, describes the possibility to getting into Alzheimer’s disease (AD) information from the pre-clinical or non-preclinical trial datasets using Machine Learning Classifier (ML) approaches.

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Notes

  1. 1.

    The ADNI, a public-private partnership, was established in 2003 under the guidance of Principal Investigator Michael W. Weiner, MD. A comprehensive list of ADNI can be found at http://adni.loni.usc.edu/wpcontent/uploads/how/_to/_apply.

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Authors and Affiliations

  1. Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh

    Jannatul Loba, Md. Rajib Mia, Imran Mahmud & Md. Julkar Nayeen Mahi

  2. School of Information Systems, Queensland University of Technology, Brisbane, Australia

    Md. Whaiduzzaman

  3. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada

    Kawsar Ahmed

Authors
  1. Jannatul Loba
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  2. Md. Rajib Mia
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  3. Imran Mahmud
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  4. Md. Julkar Nayeen Mahi
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  5. Md. Whaiduzzaman
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  6. Kawsar Ahmed
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Corresponding authors

Correspondence to Md. Rajib Mia or Md. Julkar Nayeen Mahi .

Editor information

Editors and Affiliations

  1. School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford, UK

    Muhammad Younas

  2. Department of Computer Science, Faculty of Engineering and Informatics, University of Bradford, Bradford, UK

    Irfan Awan

  3. Université Paris Cité, Paris, France

    Salima Benbernou

  4. Computer Science Department, Faculty of Mathematics and Informatics, Western University of Timisoara, Timisoara, Romania

    Dana Petcu

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Loba, J., Mia, M.R., Mahmud, I., Mahi, M.J.N., Whaiduzzaman, M., Ahmed, K. (2023). MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Disease Prediction Through Machine Learning Classifiers. In: Younas, M., Awan, I., Benbernou, S., Petcu, D. (eds) The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Deep-BDB 2023. Lecture Notes in Networks and Systems, vol 768. Springer, Cham. https://doi.org/10.1007/978-3-031-42317-8_6

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