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Early Diagnosis of Parkinson’s Disease Progression

Vadalia, Dharesh (2020) Early Diagnosis of Parkinson’s Disease Progression. Masters thesis, Dublin, National College of Ireland.

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Parkinson’s Disease (PD) is triggered due to the loss of dopaminergic neurons in the substantia nigra, disrupting the neural communication of the central nervous system towards reception and response of motor and cognitive senses of the patient. PD is a progressive neural disorder which worsens with ageing. With no clearly outlined pattern posed in symptoms, it is challenging for medical practitioners to identify the disease in its prodromal stage. Inspired from the cause, this research’s objective is to predict the rate of progression based on the baseline assessment of a patient so that an appropriate treatment plan can be designed for that individual patient. Biomarkers responsible for baseline assessment are extracted from multiple pre-clinical assessments designed to capture and scale the motor and cognitive impairments experienced by PD patients during the prodromal stage. The study performs clustering of PD patients into 3 clusters marking the rate of progression based on the captured clinical feature of the patients and performance comparison of 7 different ensembled and neural network-based classification model is conducted in this study. The study aims to assist medical practitioners in early diagnosis of risk PD among patients and adopt an appropriate measure to improve patient’s quality of life.
Keywords – Parkinson’s Disease, Clinical Progression, PPMI, Gaussian Mixture Model, Multi-Class Classification

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 25 Jan 2021 15:58
Last Modified: 25 Jan 2021 15:58

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