Enhancing Early Intervention and Patient Care through Machine Learning in Alzheimer's Disease Prediction

C. Mallika, J. Vanitha, Visalatchy, S. Selvaganapathy, K. Kalavani

2025

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder with a growing prevalence worldwide. The application of machine learning (ML) for early AD prediction can enhance early diagnosis and patient care. This study employs large-scale administrative health data from the Korean National Health Insurance Service (NHIS) to develop predictive models for AD incidence. Three ML models Random Forest, Support Vector Machine, and Logistic Regression were trained using 40,736 elderly individuals’ data with 4,894 unique clinical attributes. The best-performing model achieved an area under the curve (AUC) of 0.775 for one-year predictions. Key predictive features include hemoglobin levels, age, and urine protein levels. The study highlights the potential of ML in AD prediction and its implications for early intervention.

Paper Citation

in Harvard Style

Mallika C., Vanitha J., Visalatchy., Selvaganapathy S. and Kalavani K. (2025). Enhancing Early Intervention and Patient Care through Machine Learning in Alzheimer's Disease Prediction. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 361-364. DOI: 10.5220/0013913100004919

in Bibtex Style

@conference{icrdicct`2525,
author={C. Mallika and J. Vanitha and Visalatchy and S. Selvaganapathy and K. Kalavani},
title={Enhancing Early Intervention and Patient Care through Machine Learning in Alzheimer's Disease Prediction},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={361-364},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013913100004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Enhancing Early Intervention and Patient Care through Machine Learning in Alzheimer's Disease Prediction
SN - 978-989-758-777-1
AU - Mallika C.
AU - Vanitha J.
AU - Visalatchy.
AU - Selvaganapathy S.
AU - Kalavani K.
PY - 2025
SP - 361
EP - 364
DO - 10.5220/0013913100004919
PB - SciTePress