Revolutionizing Parkinson’s Disease Diagnosis: An Advanced Data Science and Machine Learning Architecture

Esteban Gustavo Novillo Quinde; María José Montesdeoca González; Remigio Ismael Hurtado Ortiz

doi:10.1007/978-3-031-54235-0_17

Revolutionizing Parkinson’s Disease Diagnosis: An Advanced Data Science and Machine Learning Architecture

Esteban Gustavo Novillo Quinde, María José Montesdeoca González, Remigio Ismael Hurtado Ortiz

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

Abstract

The presence of speech impairment during the early stages of Parkinson’s disease has motivated several experts to try to predict whether a patient has the disease by applying various techniques based on Machine Learning. Currently, there is no adequate technique for that process. This paper proposes an analysis using three Learning Models (Support Vector Machine, Random Forest, and Multi-Layer Perceptron Neural Network with and without dimensionality reduction executing the Partial Least-Squares Discriminant Analysis). To classify healthy patients from diseased ones by applying quality measures to compare the results obtained with those acquired in related research. The dataset used also contains the Q-factor wavelet transform of each sample to increase the accuracy of the models (0 to negative and 1 to positive for this disease). This research gives way to future works, which will be in charge of improving the values achieved by optimizing the execution times using more advanced techniques.

Original language	English
Title of host publication	Information Technology and Systems - ICITS 2024
Editors	Alvaro Rocha, Jorge Hochstetter Diez, Carlos Ferras, Mauricio Dieguez Rebolledo
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	183-192
Number of pages	10
ISBN (Print)	9783031542343
DOIs	https://doi.org/10.1007/978-3-031-54235-0_17
State	Published - 2024
Event	International Conference on Information Technology and Systems, ICITS 2024 - Temuco, Chile Duration: 24 Jan 2024 → 26 Jan 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	932 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Information Technology and Systems, ICITS 2024
Country/Territory	Chile
City	Temuco
Period	24/01/24 → 26/01/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Data science
Machine Learning
Multi-Layer Perceptron Neural Network
Parkinson’s disease
Random Forest
Support Vector Machine
Vocal disorder

Access to Document

10.1007/978-3-031-54235-0_17

Cite this

Novillo Quinde, E. G., Montesdeoca González, M. J., & Hurtado Ortiz, R. I. (2024). Revolutionizing Parkinson’s Disease Diagnosis: An Advanced Data Science and Machine Learning Architecture. In A. Rocha, J. H. Diez, C. Ferras, & M. D. Rebolledo (Eds.), Information Technology and Systems - ICITS 2024 (pp. 183-192). (Lecture Notes in Networks and Systems; Vol. 932 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-54235-0_17

Novillo Quinde, Esteban Gustavo ; Montesdeoca González, María José ; Hurtado Ortiz, Remigio Ismael. / Revolutionizing Parkinson’s Disease Diagnosis : An Advanced Data Science and Machine Learning Architecture. Information Technology and Systems - ICITS 2024. editor / Alvaro Rocha ; Jorge Hochstetter Diez ; Carlos Ferras ; Mauricio Dieguez Rebolledo. Springer Science and Business Media Deutschland GmbH, 2024. pp. 183-192 (Lecture Notes in Networks and Systems).

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abstract = "The presence of speech impairment during the early stages of Parkinson{\textquoteright}s disease has motivated several experts to try to predict whether a patient has the disease by applying various techniques based on Machine Learning. Currently, there is no adequate technique for that process. This paper proposes an analysis using three Learning Models (Support Vector Machine, Random Forest, and Multi-Layer Perceptron Neural Network with and without dimensionality reduction executing the Partial Least-Squares Discriminant Analysis). To classify healthy patients from diseased ones by applying quality measures to compare the results obtained with those acquired in related research. The dataset used also contains the Q-factor wavelet transform of each sample to increase the accuracy of the models (0 to negative and 1 to positive for this disease). This research gives way to future works, which will be in charge of improving the values achieved by optimizing the execution times using more advanced techniques.",

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Novillo Quinde, EG, Montesdeoca González, MJ & Hurtado Ortiz, RI 2024, Revolutionizing Parkinson’s Disease Diagnosis: An Advanced Data Science and Machine Learning Architecture. in A Rocha, JH Diez, C Ferras & MD Rebolledo (eds), Information Technology and Systems - ICITS 2024. Lecture Notes in Networks and Systems, vol. 932 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 183-192, International Conference on Information Technology and Systems, ICITS 2024, Temuco, Chile, 24/01/24. https://doi.org/10.1007/978-3-031-54235-0_17

Revolutionizing Parkinson’s Disease Diagnosis: An Advanced Data Science and Machine Learning Architecture. / Novillo Quinde, Esteban Gustavo; Montesdeoca González, María José; Hurtado Ortiz, Remigio Ismael.
Information Technology and Systems - ICITS 2024. ed. / Alvaro Rocha; Jorge Hochstetter Diez; Carlos Ferras; Mauricio Dieguez Rebolledo. Springer Science and Business Media Deutschland GmbH, 2024. p. 183-192 (Lecture Notes in Networks and Systems; Vol. 932 LNNS).

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

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Novillo Quinde EG, Montesdeoca González MJ, Hurtado Ortiz RI. Revolutionizing Parkinson’s Disease Diagnosis: An Advanced Data Science and Machine Learning Architecture. In Rocha A, Diez JH, Ferras C, Rebolledo MD, editors, Information Technology and Systems - ICITS 2024. Springer Science and Business Media Deutschland GmbH. 2024. p. 183-192. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-54235-0_17