The School of Computer Science is pleased to present…
Machine Learning for Clinical Score Prediction from Longitudinal Dataset: A Case Study on Parkinson's Disease
MSc Thesis Proposal by: Nourin Ahmed
Date: Friday, 28 Jun 2024
Time: 11:30 am
Location: Erie Hall, Room 186
Abstract:
Accurate prediction of Parkinson's disease (PD) progression is vital for personalized treatment and effective clinical trials. This study presents a machine learning approach to predict the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III scores, quantifying motor symptom progression in PD patients. Using the longitudinal Parkinson's Progression Markers Initiative (PPMI) dataset, we examined the impact of dataset format (wide vs. cross-sectional), dimensionality reduction techniques (PCA, NMF), and regression models (Linear Regression, Random Forest, XGBoost, SVR) on prediction performance. Our findings indicate that models trained on wide-format datasets consistently outperformed those on cross-sectional data. The combination of Nonnegative Matrix Factorization (NMF) and Support Vector Regression (SVR) achieved the best performance, with a mean absolute error (MAE) of 1.91 and R^2 of 0.83. These results underscore the importance of data arrangement and highlight NMF's effectiveness in feature extraction for longitudinal datasets.
Thesis Committee:
Internal Reader: Dr. Dan Wu
External Reader: Dr. Esam Abdel-Raheem
Advisor: Dr. Ziad Kobti
