Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia. While AF is a cardiological disease, its risk factors and mechanisms are often rooted in non-cardiological comorbidities, introducing complexity in the treatment of the heterogeneous patient population. This study presents the development of a clinical decision support system (CDSS), which aims to mitigate potential challenges of the cross-disciplinarity of AF A knowledge base is implemented to capture the hierarchical nature of relevant concepts. Naiv˙e Bayes classifiers are used to predict the patient comorbidities related to AF mechanisms and risk factors based on provided symptoms. The resulting CDSS infers comorbidities with a top-k accuracy of 0.53, 0.80, and 0.88 for k=1,3 , and 5 respectively.