An exploration of new methods for metabolic syndrome examination by infrared thermography and knowledge mining.

ABSTRACT: Metabolic syndrome (MS) is a clinical syndrome with multiple metabolic disorders. As the diagnostic criteria for MS still lacking of imaging laboratory method, this study aimed to explore the differences between healthy people and MS patients through infrared thermography (IRT). However, the observation region of the IRT image is uncertain, and the research tried to solve this problem with the help of knowledge mining technology. 43 MS participants were randomly included through a cross-sectional method, and 43 healthy participants were recruited through number matching. The IRT image of each participant was segmented into the region of interest (ROI) through the preprocessing method proposed in this research, and then the ROI features were granulated by the K-means algorithm to generate t

SUBMITTER: Mi BH 

PROVIDER: S-EPMC9012989 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature