Using methods of time series data mining to recognize the influences of environmental factors on bullous pemphigoid

Bullous pemphigoid (BP) is an acute or chronic autoimmune skin disease. It is reported that poor conditions of general health and increased mortality rates are observed in BP patients, although there is a paucity of studies on the pathophysiologic reasons for the phenomena. In the study, we applied various time series data mining techniques to validate the effect of environmental factors on BP. Several statistical methods were used to explore the seasonal incidence patterns of BP, and validate the significance of the obtained patterns. The correlation measure was employed to evaluate the relationship between meteorological variables and BP incidence. The Fourier-Gaussian Decomposition (FGD) algorithm developed by our research group is a significant contribution. The FGD was also adopted to recognize the seasonal incidence distributions of BP. The study indicated that the monthly incident counts for BP significantly increased in August. The correlation measure verified strong correlation of BP incidence with high ambient temperature. Furthermore, with respect to its ability to extract hidden patterns from input sequences, the effectiveness of the FGD algorithm was compared and validated with conventional methods for time series data mining. Hence, it is expected that the mined epidemiological properties may aid in disease prevention of BP.