Name: PETER GLEISER GARCEZ
Publication date: 28/01/2025
Examining board:
Name |
Role |
|---|---|
| EVANDRO OTTONI TEATINI SALLES | Coorientador |
| KARIN SATIE KOMATI | Examinador Externo |
| PATRICK MARQUES CIARELLI | Presidente |
| RENATO ANTONIO KROHLING | Examinador Interno |
Summary: Approximately 220,000 Brazilians have Parkinson’s Disease (PD), which affects 1% to 3%of the world’s population over 65 years, according to WHO estimates. PD causes a continuous and gradual loss of dopamine-producing neurons, a neurotransmitter essential for muscle function performance, especially speech motor control, causing impairment in voice quality. This study aims to implement feature selection and machine learning hyperparameter tuning through optimization metaheuristics to identify PD using features extracted from voice signals. At first, the metaheuristic Adaptive HybridMutated Differential Evolution (A-HMDE) is applied to select features from a dataset. Next, considering the selected features, we tuned the hyperparameter of the Random Forest (RF) and k-Nearest Neighbors (kNN) models, as well as of the Convolutional Neural Network 1D (CNN 1D) model using metaheuristic. A reduction from 752 to 75 features was achieved, representing a selection rate of less than 10%, with an accuracy of 91.63% and a recall of 99.39% obtained by the RF classifier. The results demonstrate the effectiveness of the metaheuristics used for identifying Parkinson’s Disease through voice, and the need to develop datasets with unprocessed vocal signals to explore the performance of convolutional networks operating on raw signals for PD classification.
