Ria Bhavaraju

Started Apr. 3, 2021

Classification of Parkinson's Patients using Machine Learning Algorithms

Started June 4, 2021

Abstract or project description

The main question that the project is trying to answer is how do you classify Parkinson’s patients based on genetic data. Parkinson’s disease is seen in older patients, and is often misdiagnosed in the early stages. Catching the disease earlier can lead to a better quality of life, but because of the lack of symptoms early on the disease progression, early onset Parkinson's is usually mistaken for other types of diseases. Thus, catching the disease from patterns in a person’s genetics can lead to that better quality of life. The project aims to train machine learning algorithms to identify these patterns in the genes that could be indicative of Parkinson’s, by utilizing varied datasets of genetic information from Parkinson’s patients and comparing them to patients without Parkinson’s who are in the same age range. Using machine learning expands on previous research because it creates algorithms that can be applied to other patients to catch the disease in the early stages. A final addition to the project would be to compare the genetic patterns of Parkinson’s to the genetic patterns of other diseases to identify the possible correlations between the genes that are affected in order to be afflicted by the disease.