Exploring Essential Acoustic Features for Early Parkinson’s Disease Classification: A Machine Learning Study

Abstract

Parkinson’s disease (PD) is a neurological condition that affects approximately 10 million individuals globally and is ranked as the second most prevalent neurodegenerative condition after Alzheimer’s disease. Vocal disorders can be identified in approximately 90% of PD patients in the early stages of the disease. In this study, 19 machine learning (ML) algorithms were applied to a database of voice recordings of healthy individuals and individuals with PD obtained from a public repository. Different feature selection (FS) and hyperparameter optimization techniques were applied to all models for training, testing, and validation data. Among the ML algorithms, support vector machine with radial kernel, Naïve Bayes (NB), and Gaussian process classifier (GPC) yielded promising results when considering all features. Linear discriminant analysis, K neighbors classifier (KNN), extra trees classifier, GPC, and NB demonstrated excellent performance on the testing data after employing FS techniques. Decision tree classifier, KNN, and GPC emerged as the top performers when applied to the validation dataset. Our findings, derived from an extensive and chronological review of studies utilizing the same dataset, which surpass previous benchmarks, provide a comprehensive understanding of ML’s application in voice analysis to support accurate clinical decision-making.