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Validity of Machine Learning in Detecting Complicated Appendicitis in a Resource-Limited Setting: Findings from Vietnam.

Tuong-Anh Phan-MaiThanh Truc ThaiThanh Quoc MaiKiet Anh VuCong Chi MaiDung Anh Nguyen
Published in: BioMed research international (2023)
Machine learning approaches including SVM, DT, logistic, KNN, ANN, and GB have a high level of validity in classifying patients with complicated appendicitis and patients without complicated appendicitis. Among these, GB had the highest level of validity and should be used or further validated. Our study indicates the beneficial potentials of machine learning techniques in a clinical setting in general and in the diagnosis of complicated appendicitis in particular.
Keyphrases
  • machine learning
  • artificial intelligence
  • end stage renal disease
  • big data
  • newly diagnosed
  • ejection fraction
  • chronic kidney disease
  • prognostic factors
  • deep learning
  • patient reported