Advanced Multimodal Acoustic Data Pre-Processing and Deep Learning Techniques for Enhanced Parkinson's Disease Detection and Management

This work intends to create a sophisticated framework using acoustic data for Parkinson's Disease (PD) detection, so acting as a non-motor diagnostic tool. Strong data preprocessing is the main emphasis of the approach to improve data structure, accuracy, and diagnostic dependability. Mean feature extraction and redundancy elimination are achieved using methods including expectation-maximizing (EM) clustering, random forest (RF) classification, and principal component selection. Furthermore, integrated for deep classification are ensemble convolutional neural networks (ECNN). With a 20 % increase in diagnostic accuracy over traditional approaches, the system shows better performance. With a Pearson coefficient of 0.925, lower mean absolute error (0.869), and a root relative squared error (58.12 %) compared to RF, key results show ECNN's improved diagnostics precision. These findings draw important consequences for early diagnosis and non-invasive monitoring since they show the possibility of the framework to raise PD detection accuracy.