A Comparative Study of Machine Learning Techniques for the Detection of Alzheimer's Disease

The timely detection of Alzheimer's disease remains a significant challenge in the medical field, yet machine learning algorithms offer promising solutions. This study delves into the application of machine learning techniques, such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)...

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Bibliographic Details
Published in2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Vol. 10; pp. 52 - 56
Main Authors Yogesh, Kumar, Kapil, Kaur, Kanksha, Thakur, Amandeep, Nazir, Nahida, Dahiya, Omdev, Agarwal, Sarthak
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2023
Subjects
Alzheimer's disease
KNN
Machine learning
Medical diagnostic imaging
Patterns
Precision
Prediction algorithms
Predictive models
Reliability
Support vector machines
SVM
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Summary:The timely detection of Alzheimer's disease remains a significant challenge in the medical field, yet machine learning algorithms offer promising solutions. This study delves into the application of machine learning techniques, such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), to discern Alzheimer's disease from MRI brain images. The findings demonstrate KNN's exceptional accuracy of 99.76% in identifying Alzheimer's, outperforming SVM at 98.98%. This research underscores the potential of KNN and SVM in aiding early Alzheimer's detection, potentially leading to more effective treatment strategies and improved patient outcomes.
ISSN:2687-7767
DOI:10.1109/UPCON59197.2023.10434578