General health examination is an integral part of healthcare in many countries. Identifying the participants at risk is important for early warning and preventive intervention. The fundamental challenge of learning a classification model for risk prediction lies in the unlabeled data that constitutes the majority of the collected dataset. Particularly, the unlabeled data describes the participants in health examinations whose health conditions can vary greatly from healthy to very-ill. There is no ground truth for differentiating 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 majority of the data unlabeled. An efficient iterative algorithm is designed and the proof of convergence is given. Extensive experiments based on both real health examination datasets and synthetic datasets are performed to show the effectiveness and efficiency of our method.

People take regular medical examinations mostly not for discovering diseases but for having a peace of mind regarding their health status. Therefore, it is important to give them an overall feedback with respect to all the health indicators that have been ranked against the whole population. In this paper, we propose a framework of mining Personal Health Index (PHI) from a large and comprehensive geriatric medical examination (GME) dataset. We define PHI as an overall score of personal health status based on a complement probability of health risks. The health risks are calculated using the information from the cause of death (COD) dataset that is linked to the GME dataset. Especially, the highest health risk is revealed in the cases of people who had been taking GME for some years and then passed away for medical reasons. The proposed framework consists of methods in data pre-processing, feature extraction and selection, and model selection. The effectiveness of the proposed framework is validated by a set of comprehensive experiments based on the records of 102,258 participants. As the first of this kind, our work provides a baseline for further research.