Health care fraud detection using nonnegative matrix factorization

In a practical health care dataset, there are many patients with different prescriptions. A methodology for automatically identifying and clustering patients with similar symptoms is needed for health care management department to judge whether there are frauds in a large-scale clinic dataset. In this paper, we encode the clinic data with a low rank nonnegative matrix factorization algorithm to retain natural data non-negativity, thereby eliminating the need to use subtractive basis vector and encoding calculations presented in other techniques such as principal component analysis for similar feature abstraction. Result evaluations of the proposed method are conducted on a practical dataset supplied by Health Insurance Management Center of Xiamen. In our experiments, we have shown that this method is useful for health care fraud detection.