Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases

Noushath Shaffi; Viswan Vimbi; Mufti Mahmud; Karthikeyan Subramanian; Faizal Hajamohideen

doi:10.1007/978-3-031-43075-6_38

Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases

Noushath Shaffi^*, Viswan Vimbi, Mufti Mahmud, Karthikeyan Subramanian, Faizal Hajamohideen

^*Corresponding author for this work

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

10 Scopus citations

Abstract

Deep Learning (DL) techniques have shown promise in the early detection of neurodegenerative diseases due to their ability to analyze large amounts of medical data accurately. However, their reliance on massive training data may not be ideal in the healthcare industry. Therefore, this paper proposes a simple yet effective machine learning (ML) based hybrid ensemble of KNN and SVM for the early detection of Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). The proposed method is hybrid in the sense that it combines the strengths of both non-parametric and parametric approaches, resulting in a more robust and accurate classification performance. The method is tested on two popular AD databases, ADNI and OASIS, and the NTUA PD dataset. The hybrid ensemble method achieves higher accuracy and specificity for AD and PD detection, which is on par with popular DL algorithms. The source code for this work can be accessed at https://github.com/snoushath/Bagging-the-Best.git.

Original language	English
Title of host publication	Brain Informatics - 16th International Conference, BI 2023, Proceedings
Editors	Feng Liu, Hongjun Wang, Yu Zhang, Hongzhi Kuai, Emily P. Stephen
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	443-455
Number of pages	13
ISBN (Print)	9783031430749
DOIs	https://doi.org/10.1007/978-3-031-43075-6_38
State	Published - 2023
Externally published	Yes
Event	16th International Conference on Brain Informatics, BI 2023 - Hoboken, United States Duration: 1 Aug 2023 → 3 Aug 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13974 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Brain Informatics, BI 2023
Country/Territory	United States
City	Hoboken
Period	1/08/23 → 3/08/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Alzheimer’s Disease
Bagging
Ensemble Learning
KNN
Machine Learning
Parkinson Disease
SVM

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-43075-6_38

Cite this

Shaffi, N., Vimbi, V., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2023). Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases. In F. Liu, H. Wang, Y. Zhang, H. Kuai, & E. P. Stephen (Eds.), Brain Informatics - 16th International Conference, BI 2023, Proceedings (pp. 443-455). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13974 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43075-6_38

Shaffi, Noushath ; Vimbi, Viswan ; Mahmud, Mufti et al. / Bagging the Best : A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases. Brain Informatics - 16th International Conference, BI 2023, Proceedings. editor / Feng Liu ; Hongjun Wang ; Yu Zhang ; Hongzhi Kuai ; Emily P. Stephen. Springer Science and Business Media Deutschland GmbH, 2023. pp. 443-455 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e3e61f8dd2634ebdbb67c03d8ef6fdce,

title = "Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer{\textquoteright}s and Parkinson{\textquoteright}s Diseases",

abstract = "Deep Learning (DL) techniques have shown promise in the early detection of neurodegenerative diseases due to their ability to analyze large amounts of medical data accurately. However, their reliance on massive training data may not be ideal in the healthcare industry. Therefore, this paper proposes a simple yet effective machine learning (ML) based hybrid ensemble of KNN and SVM for the early detection of Alzheimer{\textquoteright}s Disease (AD) and Parkinson{\textquoteright}s Disease (PD). The proposed method is hybrid in the sense that it combines the strengths of both non-parametric and parametric approaches, resulting in a more robust and accurate classification performance. The method is tested on two popular AD databases, ADNI and OASIS, and the NTUA PD dataset. The hybrid ensemble method achieves higher accuracy and specificity for AD and PD detection, which is on par with popular DL algorithms. The source code for this work can be accessed at https://github.com/snoushath/Bagging-the-Best.git.",

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author = "Noushath Shaffi and Viswan Vimbi and Mufti Mahmud and Karthikeyan Subramanian and Faizal Hajamohideen",

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Shaffi, N, Vimbi, V, Mahmud, M, Subramanian, K & Hajamohideen, F 2023, Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases. in F Liu, H Wang, Y Zhang, H Kuai & EP Stephen (eds), Brain Informatics - 16th International Conference, BI 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13974 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 443-455, 16th International Conference on Brain Informatics, BI 2023, Hoboken, United States, 1/08/23. https://doi.org/10.1007/978-3-031-43075-6_38

Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases. / Shaffi, Noushath; Vimbi, Viswan; Mahmud, Mufti et al.
Brain Informatics - 16th International Conference, BI 2023, Proceedings. ed. / Feng Liu; Hongjun Wang; Yu Zhang; Hongzhi Kuai; Emily P. Stephen. Springer Science and Business Media Deutschland GmbH, 2023. p. 443-455 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13974 LNAI).

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

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T2 - 16th International Conference on Brain Informatics, BI 2023

AU - Shaffi, Noushath

AU - Vimbi, Viswan

AU - Mahmud, Mufti

AU - Subramanian, Karthikeyan

AU - Hajamohideen, Faizal

PY - 2023

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N2 - Deep Learning (DL) techniques have shown promise in the early detection of neurodegenerative diseases due to their ability to analyze large amounts of medical data accurately. However, their reliance on massive training data may not be ideal in the healthcare industry. Therefore, this paper proposes a simple yet effective machine learning (ML) based hybrid ensemble of KNN and SVM for the early detection of Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). The proposed method is hybrid in the sense that it combines the strengths of both non-parametric and parametric approaches, resulting in a more robust and accurate classification performance. The method is tested on two popular AD databases, ADNI and OASIS, and the NTUA PD dataset. The hybrid ensemble method achieves higher accuracy and specificity for AD and PD detection, which is on par with popular DL algorithms. The source code for this work can be accessed at https://github.com/snoushath/Bagging-the-Best.git.

AB - Deep Learning (DL) techniques have shown promise in the early detection of neurodegenerative diseases due to their ability to analyze large amounts of medical data accurately. However, their reliance on massive training data may not be ideal in the healthcare industry. Therefore, this paper proposes a simple yet effective machine learning (ML) based hybrid ensemble of KNN and SVM for the early detection of Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). The proposed method is hybrid in the sense that it combines the strengths of both non-parametric and parametric approaches, resulting in a more robust and accurate classification performance. The method is tested on two popular AD databases, ADNI and OASIS, and the NTUA PD dataset. The hybrid ensemble method achieves higher accuracy and specificity for AD and PD detection, which is on par with popular DL algorithms. The source code for this work can be accessed at https://github.com/snoushath/Bagging-the-Best.git.

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Shaffi N, Vimbi V, Mahmud M, Subramanian K, Hajamohideen F. Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases. In Liu F, Wang H, Zhang Y, Kuai H, Stephen EP, editors, Brain Informatics - 16th International Conference, BI 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 443-455. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43075-6_38