Leveraging Speech Driven Patterns Multimodal Machine Learning Framework for Accurate Early Stage Parkinson's Disease Prediction - A Survey
Vijay S. Karwande, Umesh B. Pawar, Omkar Pattnaik
Abstract
PD is progressive neurodegenerative ailment that significantly affects life quality owing to death of dopamine-generating neurons in brain's area of substantia nigra. Symptoms include trouble writing, walking & conversing. Speech patterns, gait, and EEG (Electroencephalography) signals have recently been identified as biomarkers for early Parkinson's disease identification, with speech tests being particularly helpful because approximately 90 percent of Parkinson's sufferers exhibit speech issues. As the illness worsens, the patient's voice becomes much weaker necessitating the use of non-invasive voice analysis tools. This study looks uses of approaches to ML and DL to identify & predict PD based on a variety of symptoms, such as speech data, movement shaking, movement flexibility, and ease of everyday tasks. Predictive models will be trained and tested using SVM, Naive Bayes, K-NN, RF, Logistic Regression and Decision Trees are examples of machine learning classifiers. The goal to develop a ML model that accurate examine PD by analyzing speech recordings. This study uses advanced computer techniques to provide a viable tool for beginning identification and monitoring of PD using non-invasive speech analysis.