Predicting a diagnosis of ankylosing spondylitis using primary care health records-A machine learning approach.

ABSTRACT: Ankylosing spondylitis is the second most common cause of inflammatory arthritis. However, a successful diagnosis can take a decade to confirm from symptom onset (via x-rays). The aim of this study was to use machine learning methods to develop a profile of the characteristics of people who are likely to be given a diagnosis of AS in future. The Secure Anonymised Information Linkage databank was used. Patients with ankylosing spondylitis were identified using their routine data and matched with controls who had no record of a diagnosis of ankylosing spondylitis or axial spondyloarthritis. Data was analysed separately for men and women. The model was developed using feature/variable selection and principal component analysis to develop decision trees. The decision tree with the highest aver

SUBMITTER: Kennedy J 

PROVIDER: S-EPMC10065228 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature