Alzheimer’s Disease Detection Using ML Algorithm


Year : 2024 | Volume : 15 | Issue : 03 | Page : 53-57
    By

    Neha Ghawate,

  • Shreyas Gagare,

  • Rohini Ghotale,

  • Akanksha Halle,

  • Ashwini Kamble,

  1. Assistant Professor, Department of Information Technology, Parvatibai Genba Moze College, Maharashtra, India
  2. Student, Department of Information Technology, Parvatibai Genba Moze College, Maharashtra, India
  3. Student, Department of Information Technology, Parvatibai Genba Moze College, Maharashtra, India
  4. Student, Department of Information Technology, Parvatibai Genba Moze College, Maharashtra, India
  5. Student, Department of Information Technology, Parvatibai Genba Moze College, Maharashtra, India

Abstract

A degenerative neurological state of affairs, Alzheimer’s disease (AD) gradually impairs cognitive and functional capacities, especially in people over 65. Early AD detection is crucial for efficient management and treatment prep. This study delves into novel approaches for the early detection of AD using non-invasive methods. We’ve implemented a blend of neuroimaging data analysis and machine learning algorithms to pinpoint markers indicative of the disease during its initial phases. Our methodology entails scrutinizing patterns within MRI and PET scans and correlating them with clinical assessments to refine diagnostic precision. The outcomes suggest that our approach significantly enhances early detection rates compared to conventional diagnostic techniques. We can detect small changes in the brain’s structure and operation that might occur before clinical symptoms by utilizing cutting-edge technology and predictive modeling. This research not only furthers our comprehension of Alzheimer’s Disease but also fosters a proactive and personalized approach to healthcare in neurodegenerative conditions. Our discoveries stand to aid healthcare professionals in making timelier and more accurate diagnoses, potentially enhancing patient outcomes through prompt intervention. he algorithms tested include Support Vector Machines (SVM), Random Forests, k-Nearest Neighbors (k-NN), and Convolutional Neural Networks (CNN). Each model was assessed for accuracy, sensitivity, and specificity in distinguishing between healthy controls, mild cognitive impairment (MCI), and AD patients. Experimental results reveal that CNN models applied to MRI data achieved the highest classification accuracy, demonstrating the effectiveness of deep learning techniques in detecting early structural changes associated with AD. Findings contribute to the development of robust, non-invasive diagnostic tools that leverage ML for early AD detection, paving the way for precision medicine in neurodegenerative disease management.

Keywords: Alzheimer’s Disease, Early Diagnosis, Cognitive Testing, Predictive Analytics, Medical Technology, Diagnostic Methods, Brain Disorders Prediction Algorithms

[This article belongs to Journal of Experimental & Applied Mechanics ]

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How to cite this article:
Neha Ghawate, Shreyas Gagare, Rohini Ghotale, Akanksha Halle, Ashwini Kamble. Alzheimer’s Disease Detection Using ML Algorithm. Journal of Experimental & Applied Mechanics. 2024; 15(03):53-57.
How to cite this URL:
Neha Ghawate, Shreyas Gagare, Rohini Ghotale, Akanksha Halle, Ashwini Kamble. Alzheimer’s Disease Detection Using ML Algorithm. Journal of Experimental & Applied Mechanics. 2024; 15(03):53-57. Available from: https://journals.stmjournals.com/joeam/article=2024/view=188444


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Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 14/10/2024
Accepted 30/10/2024
Published 09/12/2024
Total Visits: 78


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