Comparative Analysis of Parkinson's Disease Classification Using Deep Learning Approaches Enhanced with Optimization Techniques Versus Traditional Machine Learning Models

Abstract

Parkinson's Disease (PD) is a neurodegenerative condition that presents considerable challenges in achieving accurate early diagnosis and classification. This research explores the classification of PD through the application of advanced deep learning techniques integrated with optimization methods, compared to conventional machine learning approaches. The analysis is conducted using a diverse dataset incorporating clinical, vocal, and movement-related features to ensure comprehensive evaluation. Deep learning frameworks, such as multi-layer perceptron (MLP), Long Short-Term Memory (LSDM), the proposed deep learning model namely CNN-BiGRU were enhanced using strategies like hyperparameter optimization, regularization, and advanced gradient-based optimizers to boost performance and minimize overfitting. Similarly, traditional machine learning models, including Linear Regression, Random Tree, REP Tree, and Random Forest, were implemented and tested on the same dataset. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the curve (AUC), were used to measure and compare the performance of all models. The findings reveal that optimized deep learning models significantly surpass traditional machine learning methods in both classification accuracy and generalization. This study emphasizes the effectiveness of optimization-enhanced deep learning techniques in PD classification and their clear advantages over traditional models.