Parkinson’s Disease Detection using Ensemble Learning

Abstract

Abstract: In this decade of rapid developments in medical science, most research fail to focus on age related disorders. These are illnesses that manifest their symptoms at a far later stage, making complete recovery practically impossible. Parkinson's disease (PD) is the brain's second most prevalent neurodegenerative condition. One may claim that it is nearly incurable and causes significant suffering to people. All of this indicates that there is an impending demand for accurate, trustworthy, and expandable Parkinson's disease diagnosis. A problem of this magnitude necessitates the automation of the diagnostic to lead accurate and reliable results.Most Parkinson's disease patients have some type of speech impairment or dysphonia,making speech measures and indicators one of the most essential parts in PD prediction. The Goal of this work is to compare various machine learning models in successfully predicting the severity of Parkinson's disease and develop an effective and accurate model to help diagnose the disease accurately at an earlier stage, which could help doctors assist in cure and recovery of PD patients. We want to use the Parkinson's Telemonitoring dataset obtained from the UCI ML repository for the aforementioned purpose.Five Different Classification algorithms, including decision tree, random forest, logistic regression, support vector machine, and knearest neighbors, were used to create individual models. The Ensemble learning method was then applied to combine the predictions of these individual.