Analysis of cognitive disorders using machine learning methodsUDOVIČ, JAKOB TADEJ (Avtor) Bosnić, Zoran (Mentor) Parkinson's diseasecognitive diseasesmachine learningartificial inteligencetime-seriesclusteringThis thesis explores application of machine learning methods for classification of patients with Parkinson's disease (PD) to improve accuracy over current methods. Our aim is to present a generalised algorithm for disease progression analysis from time series data that can be applied to arbitrary data of this format. We used clinical time series data based on Parkinson's Progression Markers Initiative (PPMI) questionnaires. After normalizing and celeaning the data using modern data mining techniques, we used unsupervised clustering to identify patients' disease subtypes. After assigning the initial subtype membership to the patients' baseline visits, we tested and used the best performing supervised learning model to predict patients' disease severity for the remaining visits. For this task, we applied the support vector machine (SVM), multilayer perceptron (MLP) and random forest (RF). SVM proved to be the best solution for our problem with an accuracy of 95.06% on the test set. Finally, we model and observe patients' disease subtype changes between their consecutive visits using skip-grams and markov chains. This thesis provides a rigorous analysis of advanced machine learning techniques on time series data.20232023-03-09 15:45:00Diplomsko delo/naloga144730sl