A Feasible Solution for Detecting Parkinson’s Disease using AI

Parkinson’s disease (PD) is a long-termed, neurological disorder that causes a person to lose control over several body functions including speech. The loss of nerve cells in the part of the brain called the substantia nigra causes PD. These nerve cells or neurons create an organic chemical named dopamine which acts as a neurotransmitter between the parts of the brain and central nervous system that helps to control and co-ordinate body movements. Its detection with the help of an automated system is a subject undergoing intense study. This entails a need for incorporating a machine learning model for the early detection of PD. 

This encouraged us to provide a comparative study for state of the art machine learning implementations namely Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN), Stochastic Gradient Descent (SGD) and Gaussian Naive Bayes (GNB) are executed in these modalities with their respective datasets. Furthermore, ensemble approaches such as Random Forest Classifier (RF), Adaptive Boosting (AB) and Hard Voting (HV) are implemented.

Keywords: Parkinson’s Disease, Machine Learning, Multimodal, Ensemble Approach And Bioinformatics.