In this paper, we propose a maximum entropy method for predicting disease risks. It is based on a patient’s medical history with diseases coded in International Classification of Diseases, tenth revision, which can be used in various cases. The complete algorithm with strict mathematical derivation is given. We also present experimental results on a medical dataset, demonstrating that our method performs well in predicting future disease risks and achieves an accuracy rate twice that of the traditional method. We also perform a comorbidity analysis to reveal the intrinsic relation of diseases.