Evaluation of Machine Learning Techniques for Classification of Early Parkinson's Disease

Abstract

Parkinson's disease (PD) arises from the degeneration of neurons and the subsequent depletion of dopamine, resulting in symptoms such as tremors, muscle rigidity, and bradykinesia. Timely identification is crucial; however, existing techniques do not offer a conclusive remedy. This work aims to fill the existing gap by utilizing open-source Python-trained models to evaluate the potential of auditory data in classifying Parkinson's disease, applying a range of machine learning algorithms, such as neural networks, logistic regression, random forest, adaboost, and k-nearest neighbors, to the UCI telemonitoring dataset, which consists of 31 persons, including 23 with Parkinson's disease. The evaluation is done using parameters including accuracy, precision, and recall. The suggested framework prioritizes data preprocessing, segmentation, algorithm training, and comprehensive evaluation, highlighting the significance of data preparation and algorithmic assessment in predictive modelling for early identification of Parkinson's disease.