The diagnosis of cardiovascular illnesses uses multiple modalities in order to obtain a complete and as robust as possible assessment of the heart. However, when addressing distinct pathologies, not all information might be needed in order to achieve a confident-enough diagnosis. We propose a probabilistic machine learning method to identify the patients for which the acquisition of more complex data would be useful. We hypothesise that there exists a hierarchical relationship between modalities: echocardiography is more accessible and has a lower economical cost than other modalities (like magnetic resonance imaging (MRI)). The framework consists of two classifier models, each predicting the illness from the echocardiographic and MRI views, and a sample-weighting model that combines both predictions. This weighting model is used to decide which individuals will not need an MRI acquisition additional to the echocardiographic examination. We illustrated this on a dataset of asymptomatic individuals with an echocardiographic study (N = 480), a subset of those also includes a MRI (N = 159). We analyse the effect of being overweight on cardiac geometry. We identified that the type of remodelling depended on blood pressure: overweight combined with high blood pressure resulted in an increase of ventricular mass, while only size changes were preserved for low-pressure individuals. With our method, we established that boundary cases of the former group could be correctly classified after incorporating MRI, while it was not the case for the latter.