Many machine learning models have been developed to aid in the diagnosis of dementia, to predict dementia risk and to determine cognitive performance. While it is well known that vascular pathology is a critical contributing factor to dementia, cerebral small vessel disease is often not addressed by these methods, possibly causing impeded generalizability of the models from research setting to clinical practice. The aim of this thesis is to evaluate whether vascular pathology, as represented by white matter hyperintensities (WMHs), influences the outcome of machine learning prediction models for dementia and as such hampers generalizability of these models from the research setting to clinical practice. To answer this research question, we developed a convolutional neural network (CNN) for prediction of cognitive performance measured by MMSE and ADAS13. We used ADNI data and included all subject timepoints that adhered to our inclusion criteria (N = 4846). Next, we derived gray matter density maps (GMDMs) and WMH density maps (WMHDMs) from T1 and FLAIR MRI scans using voxel-based morphometry. Six CNN models were implemented, differing in input: model 1 (GMDM), model 2 (GMDM, age, sex), model 3 (WMHDM), model 4 (WMHDM, age, sex), model 5 (GMDM, WMHDM, age, sex) and model 6 (GMDM, log(WMH ratio), age, sex). WMH ratio was defined as WMH volume corrected for intracranial volume. We split the data into a low, middle and high WMH load group based on WMH ratio tertiles and performed two sets of experiments. First, we trained all models on the low WMH load group with the intention of testing on the high WMH load group as a measure of generalizability. Second, we trained model 2 and 5 on data from all WMH load groups. For these sets of experiments, we split both the low WMH load group and all WMH load groups into a train, validation and test set. The models trained on the low WMH load group obtained a similar performance to prediction of a constant value and prediction by a model developed on randomized cognitive scores. These models likely suffered from the underrepresentation of lower cognitive performance data (i.e. high ADAS13 scores and low MMSE scores). This is supported by the fact that model 2 and 5 outperformed constant value prediction when trained on data from all WMH load groups. The current models did in general not benefit from adding WMHDM input to the model, while they did benefit slightly from log(WMH ratio) information. However, we were unable to answer our research question due to the suboptimal performance of the models. Our future research will focus on revisiting our research question through brain age prediction in order to improve generalizability of ML methods for AD to clinical populations with prominent vascular pathology.