Early detection of Parkinson Disease via speech analysis using SVC, XG BOOST, GRADIENT BOOSTING CLASSIFIER

Parkinson’s disease is the second most prevalent neurological disorder. It has a significant impact on quality of life and is currently incurable. $90 \%$ of Parkinson’s patients experience speech problems. The medical dataset contains several data kinds, including text, numbers, and photos, ready to be discovered. Big Data enables decision-makers to get valuable insights by rapidly and fully analyzing massive volumes of data. Data mining may reveal hidden patterns in massive datasets, making it a crucial talent. Machine learning algorithms (MLAs) can enhance lifespan and quality of life for older Parkinson’s patients by detecting illnesses early on. This work investigates several MLAs to enhance dataset performance and facilitate early illness prediction, leading to timely medical treatments. After a comprehensive comparison, it determines which MLA is the most accurate.