Early Detection of Parkinson’s Disease Using Novel Deep Learning Frameworks Optimized with Advanced Techniques and Machine Learning Models Evaluated through Performance Metrics

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative condition that frequently remains undiagnosed in its early stages due to subtle and overlapping symptoms. This study presents an innovative approach that integrates advanced deep learning architectures, optimization techniques, and machine learning models to enhance the accuracy of early PD detection. By utilizing performance metrics for model evaluation and comparison, the research identifies the most effective methods for achieving precise and reliable diagnoses. The proposed framework exhibits exceptional performance in differentiating early-stage Parkinson’s cases from healthy individuals, contributing to improved clinical decision-making and enabling timely interventions. Experimental results provide to prove the proposed frame work techniques using ML and DL with better optimizations and its performance metrics.