Diseases like hyperuricemia and hysteromyoma along with prediabetes (a serious health condition) are causing more suffering and hardship than ever before. Recently, computerized non-invasive diagnostic methods inspired by Traditional Chinese Medicine (TCM) have proved to be reasonable and effective using the face and/or tongue to perform disease detection. These methods no longer require bodily fluids to be extracted (e.g., a blood test), which further relieves the pain of patients and allows doctors to focus on more labor intensive activities. In this paper, we propose a novel classifier based on the fusion of the linear discriminant analysis (LDA) and the sparse representation based classifier (SRC) named L-SRC, to perform disease detection. Specifically, we collect facial images using a non-invasive capture device from those suffering from hyperuricemia, hysteromyoma and prediabetes, and feed it to the L-SRC classifier to perform classification. The experimental results show that L-SRC can discriminate samples belonging to the three classes with healthy control more effectively, achieving accuracies of 72%, 70.95% and 76.60% respectively. This indicates a promising application prospect in the future.