Study of Disease Networks Based on Association Rule Mining from Physical Examination Database

Association rule mining has been well researched as a data mining method for discovering new and interesting knowledge in large databases. In this study, association rule mining technique was used to discover human disease-disease associations from a physical examination dataset. These disease-disease associations were visualized by networks, which have become a holistic approach used to understand complex relationships among diseases. The results showed that 47 percent of 247073 individuals suffered from dyslipidemia, while 24 percent suffered from fatty liver. Moreover, dyslipidemia was highly related to various diseases and physical signs, such as obstructive sleep apnea syndrome (confidence=0.83, lift=1.76), unusually high glutamyltransferase (confidence=0.82, lift=1.73) and hyperviscosity (confidence=0.81, lift=1.72). The strongest rule discovered in this study was (hystera space-occupying lesions ⇒ hysteromyoma) with a confidence of 0.99 and a lift of 18.99. Meanwhile, some novel relationships were extracted. For example, an enlarged gallbladder closely related to prostatic hyperplasia. This rule had a confidence of 0.78 and a lift of 41.49, which means among the patients with enlarged gallbladders, 78 percent also suffered from prostatic hyperplasia. This paper discovered many novel associations, some of which were rarely reported and even if reported in previous studies, but the perspectives and results were still not consistent. Therefore, disease association networks are valuable for clinicians and medical researchers to examine the relationships among diseases and physical signs.