Early Detection of Alzheimer’s Disease Using Machine Learning Techniques

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Year : August 20, 2024 at 3:42 pm | [if 1553 equals=””] Volume :14 [else] Volume :14[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : 02 | Page : 32-43

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K.Surendra Reddy, Kuruva Ramanjaneyulu, Daggulu Lakshmi Charitha, M.Rani, K.Mohammad Muddasir, Ch.Thirupathamma, N. Naveen,

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  1. Associate Professor, Student, Student, Student, Student, Student, Student Department of Computer Science and Engineering Indira Institute of Technology & Sciences, Department of Computer Science and Engineering Indira Institute of Technology & Sciences, Department of Computer Science and Engineering Indira Institute of Technology & Sciences, Department of Computer Science and Engineering Indira Institute of Technology & Sciences, Department of Computer Science and Engineering Indira Institute of Technology & Sciences, Department of Computer Science and Engineering Indira Institute of Technology & Sciences, Department of Computer Science and Engineering Indira Institute of Technology & Sciences Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh India, India, India, India, India, India

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Abstract

nAlzheimer’s Disease (AD) is a progressive neurodegenerative condition impacting a large global population. Detecting AD early is critical for timely intervention and effective management. Conventional diagnostic approaches involve cognitive assessments and neuroimaging, which are often lengthy, costly, and prone to human error. In this paper, we propose a novel approach for early detection of AD using machine learning techniques applied to multimodal data, including neuroimaging, cognitive assessments, and biomarkers. Our methodology involves preprocessing and feature extraction from these data sources, followed by the application of supervised and unsupervised machine learning algorithms for classification and clustering tasks. Our experimental results robustly showcase the effectiveness of our methodology, achieving remarkable accuracy in distinguishing among healthy individuals, those diagnosed with mild cognitive impairment (MCI), and patients suffering from Alzheimer’s disease (AD). These findings underscore the significant strides made in diagnostic precision and patient stratification within neurocognitive disorders. Moreover, our study elucidates the far-reaching implications of these advancements, poised to enhance clinical care practices and propel further breakthroughs in Alzheimer’s disease research, thereby offering promising avenues for improved patient outcomes and deeper insights into disease pathology.

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Keywords: Alzheimer’s disease, machine learning, early detection, multimodal data, neuroimaging, cognitive assessments, biomarkers, supervised learning, unsupervised learning

n[if 424 equals=”Regular Issue”][This article belongs to Research & Reviews: A Journal of Neuroscience(rrjons)]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Research & Reviews: A Journal of Neuroscience(rrjons)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article: K.Surendra Reddy, Kuruva Ramanjaneyulu, Daggulu Lakshmi Charitha, M.Rani, K.Mohammad Muddasir, Ch.Thirupathamma, N. Naveen. Early Detection of Alzheimer’s Disease Using Machine Learning Techniques. Research & Reviews: A Journal of Neuroscience. August 20, 2024; 14(02):32-43.

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How to cite this URL: K.Surendra Reddy, Kuruva Ramanjaneyulu, Daggulu Lakshmi Charitha, M.Rani, K.Mohammad Muddasir, Ch.Thirupathamma, N. Naveen. Early Detection of Alzheimer’s Disease Using Machine Learning Techniques. Research & Reviews: A Journal of Neuroscience. August 20, 2024; 14(02):32-43. Available from: https://journals.stmjournals.com/rrjons/article=August 20, 2024/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Original Research

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Volume 14
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02
Received June 11, 2024
Accepted July 5, 2024
Published August 20, 2024

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