Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)

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Comparative Analysis of Machine Learning Models for Alzheimer’s Disease Prediction

Authors
Shaktivel Thevar1, Shruthi Menon1, Ovaiz Shaikh1, Devansh Prasade1, Shruti Shruti1, *
1Dept. of Computer Engineering, SIES Graduate School of Technology, Navi Mumbai, India
*Corresponding author. Email: shrutipk@sies.edu.in
Corresponding Author
Shruti Shruti
Available Online 7 October 2025.
DOI
10.2991/978-94-6463-852-3_33How to use a DOI?
Keywords
Alzheimer’s Disease; Machine Learning; MRI; Classification; SVM; Early Detection; OASIS Dataset
Abstract

Alzheimer’s Disease (AD) is a neurodegenerative disorder that significantly impacts cognitive functions, particularly in the aging population. Early diagnosis of AD remains a critical challenge due to the disease’s subtle initial symptoms and the complexity of available clinical data. In recent years, machine learning (ML) has emerged as a powerful tool for identifying early signs of AD using neuroimaging and cognitive metrics. This paper presents a comparative analysis of various ML models—including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Convolutional Neural Networks (CNN)—to evaluate their effectiveness in detecting Alzheimer’s at an early stage. The models were trained and tested on the publicly available OASIS dataset, and their performance was assessed using metrics such as accuracy, precision, recall, F1-score, and AUC. The findings highlight the strengths and limitations of each model, offering insights into their real-world clinical potential.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
Series
Advances in Intelligent Systems Research
Publication Date
7 October 2025
ISBN
978-94-6463-852-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-852-3_33How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shaktivel Thevar
AU  - Shruthi Menon
AU  - Ovaiz Shaikh
AU  - Devansh Prasade
AU  - Shruti Shruti
PY  - 2025
DA  - 2025/10/07
TI  - Comparative Analysis of Machine Learning Models for Alzheimer’s Disease Prediction
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 517
EP  - 535
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_33
DO  - 10.2991/978-94-6463-852-3_33
ID  - Thevar2025
ER  -