Improving Early Alzheimer’s Disease Diagnosis Using Machine Learning on Clinical and Demographic Data

DOI

10.2991/978-94-6239-664-7_3How to use a DOI?

Keywords

Alzheimer’s Disease; Classification Models; demographic; Machine Learning

Abstract

Early diagnosis of Alzheimer’s disease (AD) significantly improves patient outcomes, yet most studies achieving greater than 95% accuracy rely on expensive MRI/PET imaging or deep learning models that are infeasible in low-resource settings. This work demonstrates that simple, interpretable machine learning models can achieve comparable performance using only routinely collected, low-cost clinical and demographic data (n=2149). Five classifiers (Logistic Regression, Random Forest, Gradient Boosting, SVC, and soft Voting Classifier) were compared using 10-fold cross-validation with SMOTE applied only on training folds to address class imbalance. Gradient Boosting yielded the best results: 95.81% accuracy, 95.5% precision, 95.0% recall, 95.2% F1- score, and 0.99 AUC. By requiring no neuroimaging or specialised infrastructure, the proposed approach provides an immediately deployable screening tool for primary-care and resource-limited environments. Limitations include moderate dataset size and limited ethnocultural diversity. Future work will focus on external validation on multi-ethnic cohorts (ADNI, OASIS), integration of low-cost multimodal data, and transfer learning to enhance generalisability.

Copyright

© 2026 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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TY  - CONF
AU  - Abdur Rahman
AU  - Md. Shahriar Mannan Prottoy
AU  - Mahtab Chowdhury
AU  - Md. Hasibul Hasan Shanto
AU  - Mohammad Armanul Hoque
AU  - Sarmin Rahman Mim
PY  - 2026
DA  - 2026/06/08
TI  - Improving Early Alzheimer’s Disease Diagnosis Using Machine Learning on Clinical and Demographic Data
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 21
EP  - 33
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_3
DO  - 10.2991/978-94-6239-664-7_3
ID  - Rahman2026
ER  -