General health examination is an integral part of healthcare in many countries. Identifying the participants at risk isimportant for early warning and preventive intervention. The fundamental challenge of learning a classification model for risk predictionlies in the unlabeled data that constitutes the majority of the collected dataset. Particularly, the unlabeled data describes theparticipants in health examinations whose health conditions can vary greatly from healthy to very-ill. There is no ground truth fordifferentiating their states of health. In this paper, we propose a graph-based, semi-supervised learning algorithm called SHG-Health(Semi-supervised Heterogeneous Graph on Health) for risk predictions to classify a progressively developing situation with the majorityof the data unlabeled. An efficient iterative algorithm is designed and the proof of convergence is given. Extensive experiments basedon both real health examination datasets and synthetic datasets are performed to show the effectiveness and efficiency of our method.