PubMed · 1505701
Evaluating patients with chest pain using classification and regression trees.
Abstract
We collected data on 320 patients complaining to their general practitioner of a new episode of chest pain, discomfort or oppression. Relationships were examined between initial signs and symptoms and a follow-up diagnosis after a period of 2 weeks to 2 months. The data were analysed with CART, a statistical decision theory software package. In our first run, the number of misclassifications by CART was 56%. After regrouping of the data and diagnostic categories, there were 37% misclassifications. The most discriminating variable turned out to be pain on palpation. When comparing each of five diagnostic groups to all others, we found a positive predictive value of 27% for gastrointestinal diseases, 72% for cardiovascular disorders, 69% for respiratory diseases, 58% for psychopathology and 73% for chest wall pathology. The CART methodology needs further investigation and testing before any clinical application will be possible in general practice.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
F Buntinx, J Truyen, P Embrechts, G Moreel, R Peeters. 1992. Evaluating patients with chest pain using classification and regression trees.. https://doi.org/10.1093/fampra%2F9.2.149
Cite the original work for its findings. Save a collection to share your selection of sources.