With the development of the Internet of Things (IoT) technology, its application in the medical field becomes more and more extensive. However, with a dramatic increase in medical data obtained from the IoT-based health service system, labeling a large number of medical data requires high cost and relevant domain knowledge. Therefore, how to use a small number of labeled medical data reasonably to build an efficient and high-quality clinical decision support model in the IoT-based platform has been an urgent research topic. In this paper, we propose a novel semi-supervised learning approach in association with generative adversarial networks (GANs) for supporting clinical decision making in the IoT-based health service system. In our approach, GAN is adopted to not only increase the number of labeled data but also to compensate the imbalanced labeled classes with additional artificial data in order to improve the semi-supervised learning performance. Extensive evaluations on a collection of benchmarks and real-world medical datasets show that the proposed technique outperforms the others and provides a potential solution for practical applications.