Insomnia discriminant analysis based on real-world clinical data

A new data preprocessing method based on the real-world medical database was proposed, which can change unstructured data into structured data. Supervised algorithms and semi-supervised algorithms were utilized to verify the effectiveness of the clinical features which were obtained through our data preprocessing method. From the experimental results on the real world dataset, it is found that both supervised classification and semi-supervised algorithms can get a better result based on the clinical symptom features trained from our data preprocessing method. And it is found that the label propagation algorithm not only achieves a great stability on the real Chinese medicine database when compared with classical classification algorithm, but also obtains good results when the ratio is low.