An innovative decision tree algorithm was developed to classify patients with acute cholecystitis based solely on laboratory parameters. Analyzing 1,352 cases, the algorithm showed an impressive 82.17% accuracy in predicting the need for surgery and 73.86% accuracy for identifying gangrenous cholecystitis. Key parameters included the platelet-to-lymphocyte ratio, C-reactive protein levels, and patient age, enabling efficient distinction between uncomplicated and complicated cases, and facilitating timely treatment to enhance patient outcomes and reduce healthcare costs.
Journal Article by Sezikli İ, Tutan MB (…) Topcu R et 2 al. in Ulus Travma Acil Cerrahi Derg