Comparison of different neural network algorithms in the diagnosis of acute appendicitis
- PMID: 8666475
- DOI: 10.1016/0020-7101(95)01147-1
Comparison of different neural network algorithms in the diagnosis of acute appendicitis
Abstract
Four different neural network algorithms, binary adaptive resonance theory (ART1), self-organizing map, learning vector quantization and back-propagation, were compared in the diagnosis of acute appendicitis with different parameter groups. The results show that supervised learning algorithms learning vector quantization and back-propagation were better than unsupervised algorithms in this medical decision making problem. The best results were obtained with the learning vector quantization. The self-organizing map algorithm showed good specificity, but this was in conjunction with lower sensitivity. The best parameter group was found to be the clinical signs. It seems beneficial to design a decision support system which uses these methods in the decision making process.
Similar articles
-
Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.Surgery. 2011 Jan;149(1):87-93. doi: 10.1016/j.surg.2010.03.023. Epub 2010 May 13. Surgery. 2011. PMID: 20466403
-
Diagnosis of acute appendicitis in two databases. Evaluation of different neighborhoods with an LVQ neural network.Methods Inf Med. 1998 Jan;37(1):59-63. Methods Inf Med. 1998. PMID: 9550848
-
Is neural network better than statistical methods in diagnosis of acute appendicitis?Stud Health Technol Inform. 1997;43 Pt A:377-81. Stud Health Technol Inform. 1997. PMID: 10179576 Clinical Trial.
-
Neural networks in clinical medicine.Med Decis Making. 1996 Oct-Dec;16(4):386-98. doi: 10.1177/0272989X9601600409. Med Decis Making. 1996. PMID: 8912300 Review.
-
[Appendicitis or non-specific pain in the right iliac fossa?].Rev Prat. 2001 Oct 1;51(15):1654-6. Rev Prat. 2001. PMID: 11759534 Review. French.
Cited by
-
Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models.World J Emerg Surg. 2023 Dec 19;18(1):59. doi: 10.1186/s13017-023-00527-2. World J Emerg Surg. 2023. PMID: 38114983 Free PMC article.
-
A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis.BMC Med Inform Decis Mak. 2012 Mar 13;12:17. doi: 10.1186/1472-6947-12-17. BMC Med Inform Decis Mak. 2012. PMID: 22410346 Free PMC article.
-
Does size really matter--using a decision tree approach for comparison of three different databases from the medical field of acute appendicitis.J Med Syst. 2002 Oct;26(5):465-77. doi: 10.1023/a:1016461301710. J Med Syst. 2002. PMID: 12182210
-
Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Appl Clin Inform. 2022 May;13(3):569-582. doi: 10.1055/s-0042-1749119. Epub 2022 May 25. Appl Clin Inform. 2022. PMID: 35613914 Free PMC article.
-
Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.Langenbecks Arch Surg. 2022 Feb;407(1):51-61. doi: 10.1007/s00423-021-02348-w. Epub 2021 Oct 29. Langenbecks Arch Surg. 2022. PMID: 34716472 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Medical