EVALUATING SPEECH ANALYSIS TECHNIQUES FOR PARKINSONS DISEASE DETECTION: A COMPARISON OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS

Abstract

Parkinsons disease (PD) presents a diagnostic challenge due to its often subtle and gradual onset. Speech analysis offers a promising avenue for early detection, enabling intervention before the disease significantly progresses. This study investigates the efficacy of supervised machine learning algorithms in identifying PD using speech features. We compared the performance of Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN) for PD classification. Our findings demonstrate the superiority of ANNs, achieving a test accuracy of 97.44%, which surpasses existing benchmarks and highlights their potential for PD diagnosis. This approach leverages readily available speech data, potentially reducing reliance on expensive and time-consuming clinical procedures. This research contributes to the development of non-invasive, speech-based diagnostic tools for PD, paving the way for earlier intervention and improved patient management.