Analysis of voice recordings features for Classification of Parkinson's Disease

TL;DR

This paper explores machine learning models, especially neural networks, combined with feature selection to efficiently classify Parkinson's Disease from voice recordings, reducing feature set size without losing accuracy.

Contribution

It demonstrates that feature selection can significantly reduce features needed for accurate PD classification using machine learning, particularly neural networks.

Findings

Neural networks perform well in PD classification.

Feature selection reduces the number of features needed.

Model performance remains stable with fewer features.

Abstract

Parkinson's disease (PD) is a chronic neurodegenerative disease. Early diagnosis is essential to mitigate the progressive deterioration of patients' quality of life. The most characteristic motor symptoms are very mild in the early stages, making diagnosis difficult. Recent studies have shown that the use of patient voice recordings can aid in early diagnosis. Although the analysis of such recordings is costly from a clinical point of view, advances in machine learning techniques are making the processing of this type of data increasingly accurate and efficient. Vocal recordings contain many features, but it is not known whether all of them are relevant for diagnosing the disease. This paper proposes the use of different types of machine learning models combined with feature selection methods to detect the disease. The selection techniques allow to reduce the number of features used by the classifiers by determining which ones provide the most information about the problem. The results show that machine learning methods, in particular neural networks, are suitable for PD classification and that the number of features can be significantly reduced without affecting the performance of the models.