Early Detection of Alzheimer’s Disease Using Machine Learning Techniques

Year : 2024 | Volume :14 | Issue : 02 | Page : 32-43
By

K.Surendra Reddy,

Kuruva Ramanjaneyulu,

Daggulu Lakshmi Charitha,

M.Rani,

K.Mohammad Muddasir,

Ch.Thirupathamma,

N. Naveen,

  1. Associate Professor Department of Computer Science and Engineering Indira Institute of Technology & Sciences Andhra Pradesh India
  2. Student Department of Computer Science and Engineering Indira Institute of Technology & Sciences Andhra Pradesh India
  3. Student Department of Computer Science and Engineering Indira Institute of Technology & Sciences Andhra Pradesh India
  4. Student Department of Computer Science and Engineering Indira Institute of Technology & Sciences Andhra Pradesh
  5. Student Department of Computer Science and Engineering Indira Institute of Technology & Sciences Andhra Pradesh India
  6. Student Department of Computer Science and Engineering Indira Institute of Technology & Sciences Andhra Pradesh India
  7. Student Department of Computer Science and Engineering Indira Institute of Technology & Sciences Andhra Pradesh India

Abstract

Alzheimer’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.

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

[This article belongs to Research & Reviews: A Journal of Neuroscience(rrjons)]

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. 2024; 14(02):32-43.
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. 2024; 14(02):32-43. Available from: https://journals.stmjournals.com/rrjons/article=2024/view=168175

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Regular Issue Subscription Original Research
Volume 14
Issue 02
Received June 11, 2024
Accepted July 5, 2024
Published August 20, 2024
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