Machine Learning Approaches for Early Prediction of Alzheimer’s Disease

Zhihe Ren

2025

Abstract

Alzheimer’s disease (AD) is the most common neurodegenerative disorder, affecting more than 55 million individuals all around the world. However, effective measures are still rare, and many challenges exist, including the ambiguity of cause, multifactor interactions, lack of effective indicators for early stages, and low clinical trial success rate. As a result, recent researchers divert their attention from treatment to the early diagnosis of AD, to take precautions before the onset of AD. Traditional prediction methods, such as biomarker analysis and neuroimaging tests, have limitations in sensitivity and comprehensiveness. Recent advancements in machine learning, particularly deep learning and explainability techniques, have presented new ways to improve the accuracy and practicality of early prediction of AD. Researchers explore the integration of multimodal data fusion, self-supervised learning frameworks, and interpretable models in AD prediction. While significant progress has been made, model interpretability and clinical acceptance remain. The paper first reviews and analyses traditional methods to recognize AD and then explores the potential of emerging technologies in enhancing early AD prediction, providing insights into future research directions, such as the development of more robust and transparent machine learning models.

Paper Citation

in Harvard Style

Ren Z. (2025). Machine Learning Approaches for Early Prediction of Alzheimer’s Disease. In Proceedings of the 1st International Conference on Biomedical Engineering and Food Science - Volume 1: BEFS; ISBN 978-989-758-789-4, SciTePress, pages 33-38. DOI: 10.5220/0014386300004933

in Bibtex Style

@conference{befs25,
author={Zhihe Ren},
title={Machine Learning Approaches for Early Prediction of Alzheimer’s Disease},
booktitle={Proceedings of the 1st International Conference on Biomedical Engineering and Food Science - Volume 1: BEFS},
year={2025},
pages={33-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014386300004933},
isbn={978-989-758-789-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Biomedical Engineering and Food Science - Volume 1: BEFS
TI - Machine Learning Approaches for Early Prediction of Alzheimer’s Disease
SN - 978-989-758-789-4
AU - Ren Z.
PY - 2025
SP - 33
EP - 38
DO - 10.5220/0014386300004933
PB - SciTePress