Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease

Noushath Shaffi; Faizal Hajamohideen; Abdelhamid Abdesselam; Mufti Mahmud; Karthikeyan Subramanian

doi:10.1007/978-3-031-24801-6_16

Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease

Noushath Shaffi^*
, Faizal Hajamohideen
, Abdelhamid Abdesselam
, Mufti Mahmud
, Karthikeyan Subramanian

^*Corresponding author for this work

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

25 Scopus citations

Abstract

Machine Learning (ML) techniques remain a massively influential tool in the Computer-Aided Diagnosis (CAD) of several health applications. Mainly due to its ability to rapid learning of end-to-end models accurately using compound data. Recent years have seen an extensive application of Deep Learning (DL) models in solving the 4-way classification of Alzheimer’s Disease (AD) and achieved good results too. However, traditional machine learning classifiers such as KNN, XGBoost, SVM, etc perform either the same or better than the DL models and usually require less data for training. This property is very useful when it comes to medical applications which is characterized by unavailability of large labelled datasets. In this paper, we demonstrate the application of state-of-the-art ML classifiers in the 4-way classification of AD using the OASIS dataset. Furthermore, an ensemble classifier model is proposed based on ML models. The proposed ensemble classifier achieved an accuracy of 94.92% which is approximately 5% accuracy increase compared to individual classifier approach. The source code used in this work are publicly available at: https://github.com/snoushath/AII2022.git

Original language	English
Title of host publication	Applied Intelligence and Informatics - Second International Conference, AII 2022, Proceedings
Editors	Mufti Mahmud, Cosimo Ieracitano, Nadia Mammone, Francesco Carlo Morabito, M. Shamim Kaiser
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	219-230
Number of pages	12
ISBN (Print)	9783031248009
DOIs	https://doi.org/10.1007/978-3-031-24801-6_16
State	Published - 2022
Externally published	Yes
Event	2nd International Conference on Applied Intelligence and Informatics, AII 2022 - Reggio Calabria, Italy Duration: 1 Sep 2022 → 3 Sep 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1724 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	2nd International Conference on Applied Intelligence and Informatics, AII 2022
Country/Territory	Italy
City	Reggio Calabria
Period	1/09/22 → 3/09/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Alzheimer’s Disease
Ensemble Classifier
K-Nearest Neighbor
Machine Learning
Random Forest
Support Vector Machine
XGBoost

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-031-24801-6_16

Cite this

Shaffi, N., Hajamohideen, F., Abdesselam, A., Mahmud, M., & Subramanian, K. (2022). Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease. In M. Mahmud, C. Ieracitano, N. Mammone, F. C. Morabito, & M. S. Kaiser (Eds.), Applied Intelligence and Informatics - Second International Conference, AII 2022, Proceedings (pp. 219-230). (Communications in Computer and Information Science; Vol. 1724 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-24801-6_16

Shaffi, Noushath ; Hajamohideen, Faizal ; Abdesselam, Abdelhamid et al. / Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease. Applied Intelligence and Informatics - Second International Conference, AII 2022, Proceedings. editor / Mufti Mahmud ; Cosimo Ieracitano ; Nadia Mammone ; Francesco Carlo Morabito ; M. Shamim Kaiser. Springer Science and Business Media Deutschland GmbH, 2022. pp. 219-230 (Communications in Computer and Information Science).

@inproceedings{36b11094e2024a198556f10802ba31b2,

title = "Ensemble Classifiers for a 4-Way Classification of Alzheimer{\textquoteright}s Disease",

abstract = "Machine Learning (ML) techniques remain a massively influential tool in the Computer-Aided Diagnosis (CAD) of several health applications. Mainly due to its ability to rapid learning of end-to-end models accurately using compound data. Recent years have seen an extensive application of Deep Learning (DL) models in solving the 4-way classification of Alzheimer{\textquoteright}s Disease (AD) and achieved good results too. However, traditional machine learning classifiers such as KNN, XGBoost, SVM, etc perform either the same or better than the DL models and usually require less data for training. This property is very useful when it comes to medical applications which is characterized by unavailability of large labelled datasets. In this paper, we demonstrate the application of state-of-the-art ML classifiers in the 4-way classification of AD using the OASIS dataset. Furthermore, an ensemble classifier model is proposed based on ML models. The proposed ensemble classifier achieved an accuracy of 94.92\% which is approximately 5\% accuracy increase compared to individual classifier approach. The source code used in this work are publicly available at: https://github.com/snoushath/AII2022.git",

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Shaffi, N, Hajamohideen, F, Abdesselam, A, Mahmud, M & Subramanian, K 2022, Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease. in M Mahmud, C Ieracitano, N Mammone, FC Morabito & MS Kaiser (eds), Applied Intelligence and Informatics - Second International Conference, AII 2022, Proceedings. Communications in Computer and Information Science, vol. 1724 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 219-230, 2nd International Conference on Applied Intelligence and Informatics, AII 2022, Reggio Calabria, Italy, 1/09/22. https://doi.org/10.1007/978-3-031-24801-6_16

Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease. / Shaffi, Noushath; Hajamohideen, Faizal; Abdesselam, Abdelhamid et al.
Applied Intelligence and Informatics - Second International Conference, AII 2022, Proceedings. ed. / Mufti Mahmud; Cosimo Ieracitano; Nadia Mammone; Francesco Carlo Morabito; M. Shamim Kaiser. Springer Science and Business Media Deutschland GmbH, 2022. p. 219-230 (Communications in Computer and Information Science; Vol. 1724 CCIS).

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

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AU - Hajamohideen, Faizal

AU - Abdesselam, Abdelhamid

AU - Mahmud, Mufti

AU - Subramanian, Karthikeyan

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A2 - Ieracitano, Cosimo

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Shaffi N, Hajamohideen F, Abdesselam A, Mahmud M, Subramanian K. Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease. In Mahmud M, Ieracitano C, Mammone N, Morabito FC, Kaiser MS, editors, Applied Intelligence and Informatics - Second International Conference, AII 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 219-230. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-24801-6_16

Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this