Resumen
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.
Idioma original | Inglés |
---|---|
Título de la publicación alojada | Information Technology and Systems - ICITS 2024 |
Editores | Alvaro Rocha, Jorge Hochstetter Diez, Carlos Ferras, Mauricio Dieguez Rebolledo |
Editorial | Springer Science and Business Media Deutschland GmbH |
Páginas | 183-192 |
Número de páginas | 10 |
ISBN (versión impresa) | 9783031542343 |
DOI | |
Estado | Publicada - 2024 |
Evento | International Conference on Information Technology and Systems, ICITS 2024 - Temuco, Chile Duración: 24 ene. 2024 → 26 ene. 2024 |
Serie de la publicación
Nombre | Lecture Notes in Networks and Systems |
---|---|
Volumen | 932 LNNS |
ISSN (versión impresa) | 2367-3370 |
ISSN (versión digital) | 2367-3389 |
Conferencia
Conferencia | International Conference on Information Technology and Systems, ICITS 2024 |
---|---|
País/Territorio | Chile |
Ciudad | Temuco |
Período | 24/01/24 → 26/01/24 |
Nota bibliográfica
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.