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 proceedingConference contribution

5 Citations (Scopus)

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 languageEnglish
Title of host publicationApplied Intelligence and Informatics - Second International Conference, AII 2022, Proceedings
EditorsMufti Mahmud, Cosimo Ieracitano, Nadia Mammone, Francesco Carlo Morabito, M. Shamim Kaiser
PublisherSpringer Science and Business Media Deutschland GmbH
Pages219-230
Number of pages12
ISBN (Print)9783031248009
DOIs
Publication statusPublished - 2022
Event2nd International Conference on Applied Intelligence and Informatics, AII 2022 - Reggio Calabria, Italy
Duration: Sept 1 2022Sept 3 2022

Publication series

NameCommunications in Computer and Information Science
Volume1724 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference2nd International Conference on Applied Intelligence and Informatics, AII 2022
Country/TerritoryItaly
CityReggio Calabria
Period9/1/229/3/22

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

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