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This research aims to reduce dropout rates in higher education by developing a machine learning model to predict churn early, enabling timely interventions. The most important research findings of the thesis are summarized as follow: - The overall dropout rate at the University of Banja Luka, Bosnia and Herzegovina, was nearly half of enrolled students between the 2007/08 and 2018/19 academic years, with rates showing an increase over time. - Half of the student churn occurs within the first year of enrollment, with significantly higher rates in three-year study programs compared to four-year programs. - The results suggest that at least one-third of student churn could potentially be prevented, as it is attributable to institutional factors. - The findings indicate that it is possible to predict student churn at the earliest stages of education, even when using a challenging dataset with missing data and limited pre-academic, academic, and socioeconomic features. - The use of the Histogram-based Gradient Boosting Classifier (HGBC) in the educational data field, for the first time, resulted in an attrition classification accuracy of 75% at the beginning of the first academic year and 83% by the end, outperforming other widely used models. - To validate the effectiveness of HGBC, the model was tested under varying conditions and consistently demonstrated high performance. - The application of pre- and post-hoc interpretability techniques can enhance the credibility and reliability of machine learning models in predicting student discontinuation. - The most significant predictors of churn include gender (female), student cohort, accumulated ECTS credits, scholarship status, age at enrollment, the number of successfully passed courses, program duration, and whether the student attended a Gymnasium. - Certain variables, such as gender (female) and student cohort, consistently maintained a high level of significance across the models over time. - Models with lower performance and quality exhibited a similar ranking of feature importance to those of the best-performing models.

Motivation: Quantitative MRI (qMRI) offers sensitive and specific measures to study age-related microstructural changes in the brain. However, models assessing age trajectories in qMRI brain properties are often incomparable among centers. Goal(s): Develop normative models reflecting aging trajectories and assess the impact of bi-centric, non-fully matched protocols in brain aging studies. Approach: Investigating age trajectories in cortical regions using polynomial regression models, focusing on quantitative R1, R2*, and susceptibility mapping (QSM). Results: We validated data harmonization by observing the impact on normative trajectories using bicentric data, where we noted significantly different maturation and aging inflections for R1 and R2* trajectories across cortical regions. Impact: This bi-centric, multi-parameter qMRI study investigates age-dependent variations across cortical regions, offering a valuable reference for subsequent qMRI aging research and emphasizing age effects on the cortical surface.

Motivation: Progression independent of relapse activity (PIRA) is the most frequent manifestation of disability accumulation in multiple sclerosis (MS), but the mechanisms leading to PIRA are currently unknown. Goal(s): To investigate the link between PIRA and white matter degeneration in people with MS. Approach: To compare the integrity of normal-appearing white matter (NAWM) between patients with MS who experienced PIRA versus stable patients using diffusion tensor imaging (DTI) measures from a clinical-compatible protocol. Results: Patients with PIRA exhibited significant differences in DTI-derived measures compared to stable patients: reduced fractional anisotropy and increased mean and radial diffusivity in NAWM. Impact: This study sheds light on the relationship between progression independent of relapse activity (PIRA) and white matter degeneration in people with multiple sclerosis. The results have important implications for understanding the mechanisms of disability progression in relapsing-remitting multiple sclerosis.