Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)

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Hybrid Model Optimization With Modified Metaheuristics for Parkinson’s Disease Detection

Authors
Vladimir Markovic1, *, Angelina Njegus2, Luka Jovanovic1, Tamara Zivkovic2, Dejan Jovanovic3, Djordje Mladenovic3
1Faculti of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade, 11000, Serbia
2School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade, 11000, Belgrade, Serbia
3College of Academic Studies “Dositej”, Bulevar Vojvode Putnika 7, Belgrade, 11000, Belgrade, Serbia
*Corresponding author. Email: vladimir.markovic.19@singimail.rs
Corresponding Author
Vladimir Markovic
Available Online 23 August 2024.
DOI
10.2991/978-94-6463-482-2_8How to use a DOI?
Keywords
Parkinson’s disease; Medical Data; Diagnosis; Optimization; Sinh cosh optimizer
Abstract

Parkinson’s disease, a progressive neurological disorder primarily affecting elderly males, stems from dysregulation within the extrapyramidal tracts, notably the substantia nigra, lentiform nucleus, caudate nucleus, and ruber nucleus. This condition manifests as heightened cholinergic activity in the brain, correlating with cognitive decline, gait disturbances, sleep disorders, psychiatric symptoms, and olfactory dysfunction. Early detection is crucial for enhancing patient prognosis. Although neurological damage cannot be reversed, treatment can mitigate progression. However, patients often delay seeking treatment until symptoms significantly impair daily functioning, underscoring the importance of early detection. This study investigates the fusion of long short-term memory and extreme gradient boosting classifiers to develop an early detection system utilizing noninvasive shoe-mounted sensor data for observing patient gait. A tailored optimizer is introduced to enhance classification accuracy, achieving a notable accuracy of 0.896370, surpassing other contemporary optimizers in identical conditions.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
Series
Advances in Computer Science Research
Publication Date
23 August 2024
ISBN
978-94-6463-482-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-482-2_8How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Vladimir Markovic
AU  - Angelina Njegus
AU  - Luka Jovanovic
AU  - Tamara Zivkovic
AU  - Dejan Jovanovic
AU  - Djordje Mladenovic
PY  - 2024
DA  - 2024/08/23
TI  - Hybrid Model Optimization With Modified Metaheuristics for Parkinson’s Disease Detection
BT  - Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
PB  - Atlantis Press
SP  - 102
EP  - 120
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-482-2_8
DO  - 10.2991/978-94-6463-482-2_8
ID  - Markovic2024
ER  -