FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.

Overview

Institutions: Jouf University (Saudi Arabia); Prince Sattam Bin Abdulaziz University (Saudi Arabia); University of Sfax (Tunisia); Al Baha University (Saudi Arabia)

Journal: Diagnostics (Basel, Switzerland), volume 16, issue 13, article 2029

Dates: received 19 May 2026; accepted 22 June 2026; published online 29 June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3390/diagnostics16132029 · PMID 42449811 · PMCID PMC13359660 · OpenAlex W7166532205

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: Alzheimer's / dementia (population), clinical / translational (subfield)

Methods: Machine learning, Statistics

Keywords: SDG 3, Alzheimer’s disease classification, hybrid ensemble learning, deep neural network, weight optimisation, clinical tabular data, privacy-preserving machine learning, multi-class classification

Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex

Funding: Prince Sattam bin Abdulaziz University (PSAU/2024/03/31787)

Citations: not cited yet (Europe PMC); 93 references in the paper

Abstract

Background/Objectives: The precise identification of Alzheimer’s disease (AD) stages through clinical data is crucial for early diagnosis and suitable therapy. This classification remains troublesome due to overlap in cognitive profiles across different phases of illness progression. This study presents a comprehensive and advanced diagnostic system, termed FLAME, featuring an enhanced federated learning architecture for privacy-preserving multi-institutional implementation. It provides a systematic review of machine learning (ML) and deep learning (DL) models for the classification of five stages of Alzheimer’s disease (AD). The models include cognitively normal (CN), subjective memory complaints (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD). Methods: Sixteen traditional machine learning models and eleven deep learning architectures—including FT-Transformer and NODE—were evaluated using a structured clinical dataset comprising 362 features. A hybrid ensemble was created at the probability level by combining the two top-performing models, LightGBM and a five-layer DNN. The weights of this ensemble were automatically optimised using a Genetic Algorithm (GA) with Macro-F1 as the fitness criterion, confirmed stable across 30 independent runs (w★=0.5024±0.0001). A federated learning architecture was then established, deploying the DNN across non-IID clients while keeping LightGBM centralised. We examine four distinct aggregation algorithms: FedAvg, FedProx, FedNova, and SCAFFOLD. Results: Among all deep learning architectures, FT-Transformer achieved the highest standalone performance (accuracy = 0.7810, κ = 0.7081). The five-layer deep neural network (DNN) was selected as the DL representative for the hybrid ensemble. LightGBM attained superior machine learning performance (accuracy = 0.8156, κ = 0.7537), confirmed deterministic across 10 seeds. The LightGBM vs. XGBoost difference is not statistically significant (McNemar p=0.4227). The GA-optimised hybrid ensemble (w = 0.685) surpassed both individual baselines across all evaluation metrics. The FedNova hybrid design achieved superior overall performance in federated configurations, surpassing all centralised arrangements in accuracy (accuracy = 0.8213, κ 0.7614). Conclusions: Evolutionary ensemble optimisation combined with federated learning provides a robust, scalable, and privacy-preserving solution for AD stage classification, offering a clinically viable framework for real-world multi-institutional decision-support systems. However, the AD class remains severely under-recalled across all configurations (F1 ≤ 0.21), identifying this as the primary open challenge for clinical translation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

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Data

Datasets cited

kaggle.com/datasets/sarthakkanjariya, at Kaggle; found in the text, “3.1. Dataset Description and Clinical Variables”

Data Availability Statement

The dataset used in this study is publicly available on the Kaggle platform at: https://www.kaggle.com/datasets/sarthakkanjariya/alzheimer-dataset?resource=download (accessed on 12 January 2026). This is a preprocessed tabular CSV file accessible without registration or data use agreement. No direct access to the ADNI repository was used in this study.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 8 keywords, 1 funder, 69 references.

Cite

This paper

Gasmi, K., Ammar, L. B., Krichen, M., & Alghuried, A. (2026). FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data. Diagnostics (Basel, Switzerland), 16(13), 2029. https://doi.org/10.3390/diagnostics16132029

BibTeX

@article{gasmi2026flame, author = {Gasmi, Karim and Ammar, Lassaad Ben and Krichen, Moez and Alghuried, Ahod}, title = {{FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data}}, journal = {Diagnostics (Basel, Switzerland)}, year = {2026}, month = jun, volume = {16}, number = {13}, pages = {2029}, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, issn = {2075-4418}, doi = {10.3390/diagnostics16132029}, url = {https://doi.org/10.3390/diagnostics16132029}, pmid = {42449811}, pmcid = {PMC13359660} }

RIS

TY - JOUR AU - Gasmi, Karim AU - Ammar, Lassaad Ben AU - Krichen, Moez AU - Alghuried, Ahod TI - FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data T2 - Diagnostics (Basel, Switzerland) J2 - Diagnostics (Basel) PY - 2026 DA - 2026/06/29 VL - 16 IS - 13 SP - 2029 SN - 2075-4418 PB - Multidisciplinary Digital Publishing Institute (MDPI) DO - 10.3390/diagnostics16132029 UR - https://doi.org/10.3390/diagnostics16132029 LA - en ER -

CSL-JSON

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