Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques: A Comparative Study

Mahmudul Haque Shakir; Sunipun Seemanta; Shanzida Zaman Shimu; Sherajus Salekin; Abu Naser M.D. Arman; Md Saef Ullah Miah; M. Mostafizur Rahman; Mufti Mahmud

doi:10.1109/IJCNN64981.2025.11228086

Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques: A Comparative Study

Mahmudul Haque Shakir^*
, Sunipun Seemanta
, Shanzida Zaman Shimu
, Sherajus Salekin
, Abu Naser M.D. Arman
, Md Saef Ullah Miah
, M. Mostafizur Rahman
, Mufti Mahmud

^*Corresponding author for this work

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Alzheimer's Disease (AD) remains one of the most challenging neurodegenerative disorders to diagnose due to the complexity of its underlying causes and progression patterns. The study involved Gradient Boosting, XGBoost, and Random Forests to achieve improvements in diagnostic accuracy. The study was performed on a dataset of 2,149 patient records featuring 35 attributes, showing that ensemble methods outperform conventional approaches in working with the complexity of medical data. The best model is Gradient Boosting showing the highest accuracy of 95%, which represents the possibility for the real-world application in medical diagnosis. The results enlighten about the role of preprocessing, hyperparameter tuning, and feature analysis to achieve reliability in prediction. Thereby, these findings contribute to the ever-accumulating compendium of machine learning underpinnings in medical research as they provide leads for future AD diagnostic frameworks.

Original language	English
Title of host publication	International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331510428
DOIs	https://doi.org/10.1109/IJCNN64981.2025.11228086
State	Published - 2025
Event	2025 International Joint Conference on Neural Networks, IJCNN 2025 - Rome, Italy Duration: 30 Jun 2025 → 5 Jul 2025

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
ISSN (Print)	2161-4393
ISSN (Electronic)	2161-4407

Conference

Conference	2025 International Joint Conference on Neural Networks, IJCNN 2025
Country/Territory	Italy
City	Rome
Period	30/06/25 → 5/07/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

Alzheimer's Disease (AD)
Gradient Boosting
Machine Learning
Random Forest
Unsupervised Learning
XGBoost

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN64981.2025.11228086

Cite this

Shakir, M. H., Seemanta, S., Shimu, S. Z., Salekin, S., Arman, A. N. M. D., Miah, M. S. U., Rahman, M. M., & Mahmud, M. (2025). Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques: A Comparative Study. In International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN64981.2025.11228086

Shakir, Mahmudul Haque ; Seemanta, Sunipun ; Shimu, Shanzida Zaman et al. / Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques : A Comparative Study. International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. (Proceedings of the International Joint Conference on Neural Networks).

@inproceedings{0ea44cff2e4f467498ea4ee1fe62aa77,

title = "Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques: A Comparative Study",

abstract = "Alzheimer's Disease (AD) remains one of the most challenging neurodegenerative disorders to diagnose due to the complexity of its underlying causes and progression patterns. The study involved Gradient Boosting, XGBoost, and Random Forests to achieve improvements in diagnostic accuracy. The study was performed on a dataset of 2,149 patient records featuring 35 attributes, showing that ensemble methods outperform conventional approaches in working with the complexity of medical data. The best model is Gradient Boosting showing the highest accuracy of 95\%, which represents the possibility for the real-world application in medical diagnosis. The results enlighten about the role of preprocessing, hyperparameter tuning, and feature analysis to achieve reliability in prediction. Thereby, these findings contribute to the ever-accumulating compendium of machine learning underpinnings in medical research as they provide leads for future AD diagnostic frameworks.",

keywords = "Alzheimer's Disease (AD), Gradient Boosting, Machine Learning, Random Forest, Unsupervised Learning, XGBoost",

author = "Shakir, \{Mahmudul Haque\} and Sunipun Seemanta and Shimu, \{Shanzida Zaman\} and Sherajus Salekin and Arman, \{Abu Naser M.D.\} and Miah, \{Md Saef Ullah\} and Rahman, \{M. Mostafizur\} and Mufti Mahmud",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 International Joint Conference on Neural Networks, IJCNN 2025 ; Conference date: 30-06-2025 Through 05-07-2025",

year = "2025",

doi = "10.1109/IJCNN64981.2025.11228086",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings",

address = "United States",

}

Shakir, MH, Seemanta, S, Shimu, SZ, Salekin, S, Arman, ANMD, Miah, MSU, Rahman, MM & Mahmud, M 2025, Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques: A Comparative Study. in International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings. Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., 2025 International Joint Conference on Neural Networks, IJCNN 2025, Rome, Italy, 30/06/25. https://doi.org/10.1109/IJCNN64981.2025.11228086

Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques: A Comparative Study. / Shakir, Mahmudul Haque; Seemanta, Sunipun; Shimu, Shanzida Zaman et al.
International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques

T2 - 2025 International Joint Conference on Neural Networks, IJCNN 2025

AU - Shakir, Mahmudul Haque

AU - Seemanta, Sunipun

AU - Shimu, Shanzida Zaman

AU - Salekin, Sherajus

AU - Arman, Abu Naser M.D.

AU - Miah, Md Saef Ullah

AU - Rahman, M. Mostafizur

AU - Mahmud, Mufti

PY - 2025

Y1 - 2025

N2 - Alzheimer's Disease (AD) remains one of the most challenging neurodegenerative disorders to diagnose due to the complexity of its underlying causes and progression patterns. The study involved Gradient Boosting, XGBoost, and Random Forests to achieve improvements in diagnostic accuracy. The study was performed on a dataset of 2,149 patient records featuring 35 attributes, showing that ensemble methods outperform conventional approaches in working with the complexity of medical data. The best model is Gradient Boosting showing the highest accuracy of 95%, which represents the possibility for the real-world application in medical diagnosis. The results enlighten about the role of preprocessing, hyperparameter tuning, and feature analysis to achieve reliability in prediction. Thereby, these findings contribute to the ever-accumulating compendium of machine learning underpinnings in medical research as they provide leads for future AD diagnostic frameworks.

AB - Alzheimer's Disease (AD) remains one of the most challenging neurodegenerative disorders to diagnose due to the complexity of its underlying causes and progression patterns. The study involved Gradient Boosting, XGBoost, and Random Forests to achieve improvements in diagnostic accuracy. The study was performed on a dataset of 2,149 patient records featuring 35 attributes, showing that ensemble methods outperform conventional approaches in working with the complexity of medical data. The best model is Gradient Boosting showing the highest accuracy of 95%, which represents the possibility for the real-world application in medical diagnosis. The results enlighten about the role of preprocessing, hyperparameter tuning, and feature analysis to achieve reliability in prediction. Thereby, these findings contribute to the ever-accumulating compendium of machine learning underpinnings in medical research as they provide leads for future AD diagnostic frameworks.

KW - Alzheimer's Disease (AD)

KW - Gradient Boosting

KW - Machine Learning

KW - Random Forest

KW - Unsupervised Learning

KW - XGBoost

UR - https://www.scopus.com/pages/publications/105023961820

U2 - 10.1109/IJCNN64981.2025.11228086

DO - 10.1109/IJCNN64981.2025.11228086

M3 - Conference contribution

AN - SCOPUS:105023961820

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 30 June 2025 through 5 July 2025

ER -

Shakir MH, Seemanta S, Shimu SZ, Salekin S, Arman ANMD, Miah MSU et al. Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques: A Comparative Study. In International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2025. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN64981.2025.11228086

Optimizing Alzheimer's Disease Diagnosis Using Ensemble Machine Learning Techniques: A Comparative Study

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this