Parkinson’s Disease Detection Using Machine Learning

Abstract

This research describes the design and development of a machine learning system for detecting Parkinson's disease. The system includes Python method creation, support vector machine (SVM) classifier building, and data analysis utilising UCI/Oxford university datasets[0]. Pitch, jitter, shimmer, and harmonic-to-noise ratio are among the variables retrieved from speech recordings of healthy and Parkinson's patients in the dataset. The technology also includes a mobile application that can detect Parkinson's disease from speech recordings using the classifier. Users can record their voice samples and save them locally on the device using the mobile application. The system is built on a Raspberry Pi device, which includes a microphone module that can gather voice signals and execute the classifier. The project's goal is to develop a low-cost, portable, and accurate Parkinson's disease diagnosis tool that may be used by anybody, anywhere