Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning

TL;DR

This paper reviews machine learning methods for early Parkinson's Disease detection through speech analysis, emphasizing data processing, feature extraction, and model evaluation to improve diagnostic accuracy.

Contribution

It provides a comprehensive overview of acoustic features and machine learning algorithms for PD recognition, comparing their effectiveness and identifying best practices.

Findings

Certain acoustic features significantly improve classification accuracy.

Advanced machine learning models outperform traditional methods.

Optimal feature selection enhances model efficiency.

Abstract

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts both motor and non-motor functions, including speech. Early and accurate recognition of PD through speech analysis can greatly enhance patient outcomes by enabling timely intervention. This paper provides a comprehensive review of methods for PD recognition using speech data, highlighting advances in machine learning and data-driven approaches. We discuss the process of data wrangling, including data collection, cleaning, transformation, and exploratory data analysis, to prepare the dataset for machine learning applications. Various classification algorithms are explored, including logistic regression, SVM, and neural networks, with and without feature selection. Each method is evaluated based on accuracy, precision, and training time. Our findings indicate that specific acoustic features and advanced machine-learning techniques can effectively differentiate between individuals with PD and healthy controls. The study concludes with a comparison of the different models, identifying the most effective approaches for PD recognition, and suggesting potential directions for future research.