A Review of Alzheimer’s Disease Identification by Machine Learning

R. P. Harshini () and R. Thangarajan ()
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R. P. Harshini: Kongu Engineering College
R. Thangarajan: Kongu Engineering College

A chapter in Reliability Engineering for Industrial Processes, 2024, pp 203-211 from Springer

Abstract: Abstract In the pursuit of advancing Alzheimer’s disease identification, this research employs a comprehensive approach that integrates machine learning and deep learning techniques. Support Vector Machines (SVMs) and Decision Trees serve as robust tools, providing transparency and interpretability in the analysis of diverse datasets, including genetic, clinical, and imaging information. These methods contribute to the elucidation of key factors influencing Alzheimer’s, enhancing the understanding of disease-related patterns. Furthermore, Convolutional Neural Networks (CNNs) demonstrate their efficacy in neuroimaging analysis, capturing intricate spatial dependencies crucial for precise diagnosis. The synergy of SVMs, Decision Trees, and CNNs not only improves accuracy in disease detection but also opens avenues for early intervention and targeted treatment strategies. As machine learning and deep learning continue to evolve, the amalgamation of these techniques holds promise in revolutionizing our approach to Alzheimer’s disease, offering insights that may lead to more effective interventions and improved patient outcomes.

Keywords: Alzheimer’s disease; SVM; CNN model; Slight cognitive impairment; Machine learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-55048-5_14

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DOI: 10.1007/978-3-031-55048-5_14

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