A Multi-Stage Machine Learning Pipeline for Alzheimer's Disease Classification Using Optimized Ensemble Models

Ahmet Efe Belek; Zekeriya Katılmış

doi:10.31127/tuje.1837079

A Multi-Stage Machine Learning Pipeline for Alzheimer's Disease Classification Using Optimized Ensemble Models

Abstract

Early and accurate identification of Alzheimer’s disease (AD) is critical, particularly in light of the rapidly aging global population and the substantial public health burden associated with the condition. In this study, a multi-stage and integrated machine learning pipeline was developed for AD classification. The pipeline consists of data scaling, model selection, voting ensemble creation, hyperparameter optimization, ADASYN resampling to eliminate class imbalance and Sequential Feature Selection (SFS) steps. Initially, the dataset was normalized through a combined application of MinMaxScaler and StandardScaler. Gradient Boosting, Random Forest, Bernoulli Naive Bayes, and RBF-kernel SVC were then designated as the primary candidate models. Hyperparameter optimization was performed with Optuna's Tree-structured Parzen Estimator (TPESampler) algorithm, and early discontinuation of low-performance trials was achieved with the Hyperband pruner mechanism. Subsequently, class imbalance was addressed using ADASYN-based resampling, and the most informative predictors were identified through the Sequential Feature Selection (SFS) method. The proposed pipeline was evaluated using stratified 5-fold cross-validation at each stage and an accuracy of 97.97% was achieved with the soft-voting ensemble comprising Gradient Boosting, Random Forest and Radial Basis Function (RBF) kernel Support Vector Machine models. In particular, the soft-voting ensemble models enhanced performance stability, providing more consistent decision boundaries compared with the individual base classifiers. As a result, the proposed pipeline delivers a systematic framework that ensures high accuracy, interpretability, and reproducibility in Alzheimer’s disease classification. The findings underscore the value of integrating multi-stage strategies, including data scaling, hyperparameter optimization, resampling, and feature selection, to enhance model robustness and diagnostic reliability.

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Ahmet Efe Belek
0009-0007-9536-784X
Türkiye

Zekeriya Katılmış ^*
0000-0002-2095-5483
Türkiye

Publication Date

July 6, 2026

Submission Date

December 15, 2025

Acceptance Date

May 21, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

DOI

https://doi.org/10.31127/tuje.1837079

IZ

https://izlik.org/JA92FX69WF

Cite

RIS / Bibtex

APA

Belek, A. E., & Katılmış, Z. (2026). A Multi-Stage Machine Learning Pipeline for Alzheimer’s Disease Classification Using Optimized Ensemble Models. Turkish Journal of Engineering, 10(3), 996-1009. https://doi.org/10.31127/tuje.1837079

AMA

1.Belek AE, Katılmış Z. A Multi-Stage Machine Learning Pipeline for Alzheimer’s Disease Classification Using Optimized Ensemble Models. TUJE. 2026;10(3):996-1009. doi:10.31127/tuje.1837079

Chicago

Belek, Ahmet Efe, and Zekeriya Katılmış. 2026. “A Multi-Stage Machine Learning Pipeline for Alzheimer’s Disease Classification Using Optimized Ensemble Models”. Turkish Journal of Engineering 10 (3): 996-1009. https://doi.org/10.31127/tuje.1837079.

EndNote

Belek AE, Katılmış Z (July 1, 2026) A Multi-Stage Machine Learning Pipeline for Alzheimer’s Disease Classification Using Optimized Ensemble Models. Turkish Journal of Engineering 10 3 996–1009.

IEEE

[1]A. E. Belek and Z. Katılmış, “A Multi-Stage Machine Learning Pipeline for Alzheimer’s Disease Classification Using Optimized Ensemble Models”, TUJE, vol. 10, no. 3, pp. 996–1009, July 2026, doi: 10.31127/tuje.1837079.

ISNAD

Belek, Ahmet Efe - Katılmış, Zekeriya. “A Multi-Stage Machine Learning Pipeline for Alzheimer’s Disease Classification Using Optimized Ensemble Models”. Turkish Journal of Engineering 10/3 (July 1, 2026): 996-1009. https://doi.org/10.31127/tuje.1837079.

JAMA

1.Belek AE, Katılmış Z. A Multi-Stage Machine Learning Pipeline for Alzheimer’s Disease Classification Using Optimized Ensemble Models. TUJE. 2026;10:996–1009.

MLA

Belek, Ahmet Efe, and Zekeriya Katılmış. “A Multi-Stage Machine Learning Pipeline for Alzheimer’s Disease Classification Using Optimized Ensemble Models”. Turkish Journal of Engineering, vol. 10, no. 3, July 2026, pp. 996-1009, doi:10.31127/tuje.1837079.

Vancouver

1.Ahmet Efe Belek, Zekeriya Katılmış. A Multi-Stage Machine Learning Pipeline for Alzheimer’s Disease Classification Using Optimized Ensemble Models. TUJE. 2026 Jul. 1;10(3):996-1009. doi:10.31127/tuje.1837079