Improving Alzheimer’s Disease Detection with Refined Feature Selection and XGBoost Model for Enhanced Precision

DOI

10.2991/978-94-6463-754-0_6How to use a DOI?

Keywords

Alzheimer’s disorder; early identification event; feature selection; XGBoost model; supervisor learning; classification; predictive accuracy rate; biomarkers indicators diagnostic precision

Abstract

This research study aims to a precise identification of Alzheimer’s disease by the application of feature selection method and classification schema to XGBoost. Alzheimer’s disease is a neurological illness that is only as known as it is common, diagnosed often through a process of elimination. The Timely and accurate identification of patients is crucial in the development of effective management and treatment plans. We deployed an optimized feature selection method for the identification of the most critical markers, employing a vast dataset, making the data smaller and filtering out noise. These features were then used for training an XGBoost classifier, a learning model recommended for its strong output in solving structured data issues. The designed approach was assessed through several performance metrics to designate its efficiency in distinguishing between Alzheimer’s and the counterpart disease. The outcome with the integration of optimized feature selection alongside the XGBoost model was an arriving to a higher level in diagnostic processes, thus giving the diagnosis of problem and showing a positive tool for early identification and personalized intervention in Alzheimer’s disease patients.

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.

Download article (PDF)

TY  - CONF
AU  - M. Soumya
AU  - M. Roshni Thanka
AU  - E. Bijolin Edwin
PY  - 2025
DA  - 2025/06/30
TI  - Improving Alzheimer’s Disease Detection with Refined Feature Selection and XGBoost Model for Enhanced Precision
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 48
EP  - 61
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_6
DO  - 10.2991/978-94-6463-754-0_6
ID  - Soumya2025
ER  -