Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data

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

doi:10.1109/SSCI52147.2023.10371926

Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data

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

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

10 Scopus citations

Abstract

In this study, we focus on Parkinson's Disease (PD) classification and present a comparative analysis of prominent machine learning models using two distinct and independent modalities: Magnetic Resonance Imaging (MRI) and Acoustic data. Unlike many existing works that typically focus on a single modality, our research study provides performance evaluation on the performance of various algorithms on both MRI and Acoustic data. Through a detailed investigation, we provide an understanding of how different models perform when applied to each modality individually. Furthermore, our study extends beyond this comparative framework by introducing an ensemble approach aimed at enhancing the performance of machine learning models for PD classification using the acoustic data. Notably, our ensemble approach yields around a 12 % increase in overall performance.

Original language	English
Title of host publication	2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1328-1333
Number of pages	6
ISBN (Electronic)	9781665430654
DOIs	https://doi.org/10.1109/SSCI52147.2023.10371926
State	Published - 2023
Externally published	Yes
Event	2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 - Mexico City, Mexico Duration: 5 Dec 2023 → 8 Dec 2023

Publication series

Name	2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

Conference

Conference	2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023
Country/Territory	Mexico
City	Mexico City
Period	5/12/23 → 8/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Accoustic
Ensemble
KNN
MRI
Machine Learning
Parkinson's Disease
SVM

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Human-Computer Interaction
Decision Sciences (miscellaneous)
Safety, Risk, Reliability and Quality

Access to Document

10.1109/SSCI52147.2023.10371926

Cite this

Shaffi, N., Viswan, V., Mahmud, M., Hajamohideen, F., & Subramanian, K. (2023). Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data. In 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 (pp. 1328-1333). (2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI52147.2023.10371926

Shaffi, Noushath ; Viswan, Vimbi ; Mahmud, Mufti et al. / Towards Automated Classification of Parkinson's Disease : Comparison of Machine Learning Methods using MRI and Acoustic Data. 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1328-1333 (2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023).

@inproceedings{32fc77036ea54118a3c136d2762e616f,

title = "Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data",

abstract = "In this study, we focus on Parkinson's Disease (PD) classification and present a comparative analysis of prominent machine learning models using two distinct and independent modalities: Magnetic Resonance Imaging (MRI) and Acoustic data. Unlike many existing works that typically focus on a single modality, our research study provides performance evaluation on the performance of various algorithms on both MRI and Acoustic data. Through a detailed investigation, we provide an understanding of how different models perform when applied to each modality individually. Furthermore, our study extends beyond this comparative framework by introducing an ensemble approach aimed at enhancing the performance of machine learning models for PD classification using the acoustic data. Notably, our ensemble approach yields around a 12 \% increase in overall performance.",

keywords = "Accoustic, Ensemble, KNN, MRI, Machine Learning, Parkinson's Disease, SVM",

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

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/SSCI52147.2023.10371926",

language = "English",

series = "2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1328--1333",

booktitle = "2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023",

address = "United States",

}

Shaffi, N, Viswan, V, Mahmud, M, Hajamohideen, F & Subramanian, K 2023, Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data. in 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023. 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1328-1333, 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023, Mexico City, Mexico, 5/12/23. https://doi.org/10.1109/SSCI52147.2023.10371926

Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data. / Shaffi, Noushath; Viswan, Vimbi; Mahmud, Mufti et al.
2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1328-1333 (2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023).

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

TY - GEN

T1 - Towards Automated Classification of Parkinson's Disease

T2 - 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

AU - Shaffi, Noushath

AU - Viswan, Vimbi

AU - Mahmud, Mufti

AU - Hajamohideen, Faizal

AU - Subramanian, Karthikeyan

PY - 2023

Y1 - 2023

N2 - In this study, we focus on Parkinson's Disease (PD) classification and present a comparative analysis of prominent machine learning models using two distinct and independent modalities: Magnetic Resonance Imaging (MRI) and Acoustic data. Unlike many existing works that typically focus on a single modality, our research study provides performance evaluation on the performance of various algorithms on both MRI and Acoustic data. Through a detailed investigation, we provide an understanding of how different models perform when applied to each modality individually. Furthermore, our study extends beyond this comparative framework by introducing an ensemble approach aimed at enhancing the performance of machine learning models for PD classification using the acoustic data. Notably, our ensemble approach yields around a 12 % increase in overall performance.

AB - In this study, we focus on Parkinson's Disease (PD) classification and present a comparative analysis of prominent machine learning models using two distinct and independent modalities: Magnetic Resonance Imaging (MRI) and Acoustic data. Unlike many existing works that typically focus on a single modality, our research study provides performance evaluation on the performance of various algorithms on both MRI and Acoustic data. Through a detailed investigation, we provide an understanding of how different models perform when applied to each modality individually. Furthermore, our study extends beyond this comparative framework by introducing an ensemble approach aimed at enhancing the performance of machine learning models for PD classification using the acoustic data. Notably, our ensemble approach yields around a 12 % increase in overall performance.

KW - Accoustic

KW - Ensemble

KW - KNN

KW - MRI

KW - Machine Learning

KW - Parkinson's Disease

KW - SVM

UR - https://www.scopus.com/pages/publications/85182947025

U2 - 10.1109/SSCI52147.2023.10371926

DO - 10.1109/SSCI52147.2023.10371926

M3 - Conference contribution

AN - SCOPUS:85182947025

T3 - 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

SP - 1328

EP - 1333

BT - 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 5 December 2023 through 8 December 2023

ER -

Shaffi N, Viswan V, Mahmud M, Hajamohideen F, Subramanian K. Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data. In 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1328-1333. (2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023). doi: 10.1109/SSCI52147.2023.10371926

Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this