Network representational learning for classification of patients with neurological disordersNIKOLOSKA, BISERA (Avtor) Šubelj, Lovro (Mentor) Kastrin, Andrej (Komentor) complex networksbrain networksgraph embeddingsmachine learningclassificationNeurological disorders present challenges in the realms of diagnosis and treatment, primarily due to the multifaceted nature of symptoms exhibited by affected individuals. This thesis delves into the domain of network repre- sentational learning for patient classification within the context of neurolog- ical disorders. In general, we have a dataset with two groups of patients - the typically developing patients and the patients with a neurological disorder. We an- alyze these patients by constructing graphs in which each node symbolizes an individual patient within our dataset. In further analysis of these graph representations, we use network embedding techniques, where we derive low- dimensional node representations. We look into three different algorithms; node2vec, HOPE and matrices with the pertinent graphic characteristics. After experimenting with all the hyperparameters that the algorithms use, we choose node2vec for further analysis, since it delivers highest scores. Our study extends beyond the realm of graph embeddings by employing classification models. The embeddings serve as essential input features for machine learning algorithms, including Random Forests, Logistic Regression and Support Vector Machines (SVM). This ensemble of classifiers collectively facilitates the accurate classification of patients across various neurological disorders. From the three classification models, Random Forests yielded the best results, delivering an accuracy of 90% for node2vec. The research findings underscore the potential of network representation graphs as a tool for characterizing and classifying patients afflicted with neu- rological disorders. Our comparative analysis of classification models reveals the efficacy of graph-based embeddings in enhancing diagnostic accuracy.20232023-09-15 10:25:05Diplomsko delo/naloga150261VisID: 36483COBISS_ID: 168549379sl