TY - GEN
T1 - Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease
AU - Shaffi, Noushath
AU - Hajamohideen, Faizal
AU - Abdesselam, Abdelhamid
AU - Mahmud, Mufti
AU - Subramanian, Karthikeyan
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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
AB - 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
KW - Alzheimer’s Disease
KW - Ensemble Classifier
KW - K-Nearest Neighbor
KW - Machine Learning
KW - Random Forest
KW - Support Vector Machine
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85149667291&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85149667291&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-24801-6_16
DO - 10.1007/978-3-031-24801-6_16
M3 - Conference contribution
AN - SCOPUS:85149667291
SN - 9783031248009
T3 - Communications in Computer and Information Science
SP - 219
EP - 230
BT - Applied Intelligence and Informatics - Second International Conference, AII 2022, Proceedings
A2 - Mahmud, Mufti
A2 - Ieracitano, Cosimo
A2 - Mammone, Nadia
A2 - Morabito, Francesco Carlo
A2 - Kaiser, M. Shamim
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Applied Intelligence and Informatics, AII 2022
Y2 - 1 September 2022 through 3 September 2022
ER -