Early Diagnosos of Parkinson’s Using Dimensionality Reduction Techniques

Abstract

Correct and early diagnosing Parkinson’s Disease (PD) is vital as it enables the patient to receive the proper treatment as required for the current stage of the disease. Early diagnosis is crucial, as certain treatments, such as levodopa and carbidopa, have been proven to be more effective if given in the early stages of PD. At present the diagnosis of PD is solely based on the clinical assessment of a patient’s motor symptoms. By this stage however, PD has developed to such an extent that irreversible neurological damage has already occurred, meaning the patient has no chance of recovering. By implementing the use of machine learning into the process of assessing a potential PD patient the disease can be detected and diagnosed at a much earlier stage, allowing for swift intervention, which increases the chance of PD not developing to such damaging levels in the patient. Machine Learning is a subfield of artificial intelligence that provides different technique to scientists, clinicians and patients to address and detect diseases like PD at early stage. The main symptom of PD is the vocal impairment that distinguishes from the normal person. In this study, we used a PD vocal based dataset that has 755 features The Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) techniques are used to reduce the dimensionality of the available Parkinson’s dataset to 8 optimal features. The study used four supervised machine learning algorithms, two algorithms are from the ensemble techniques, Random Forest, Adaboost Support Vector Machine and Logistic Regression. The Random Forest model with LDA and PCA shows the highest accuracy of 0.948% and 0.840% respectively.