Artificial Intelligence in Medicine
Volume 38, Issue 3 , Pages 305-318 , November 2006

Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room

  • Michael Green

      Affiliations

    • Department of Theoretical Physics, Lund University, Sölvegatan 14A, SE-22362 Lund, Sweden
    • Corresponding Author InformationCorresponding author. Tel.: +46 222 34 94; fax: +46 222 96 86.
  • ,
  • Jonas Björk

      Affiliations

    • Competence Centre for Clinical Research, Lund University Hospital, SE-22185 Lund, Sweden
  • ,
  • Jakob Forberg

      Affiliations

    • Department of Emergency Medicine, Lund University Hospital, SE-22185 Lund, Sweden
  • ,
  • Ulf Ekelund

      Affiliations

    • Department of Emergency Medicine, Lund University Hospital, SE-22185 Lund, Sweden
  • ,
  • Lars Edenbrandt

      Affiliations

    • Department of Clinical Physiology, Malmö University Hospital, SE-20502 Malmö, Sweden
  • ,
  • Mattias Ohlsson

      Affiliations

    • Department of Theoretical Physics, Lund University, Sölvegatan 14A, SE-22362 Lund, Sweden

Received 21 November 2005 ,Revised 5 July 2006 ,Accepted 12 July 2006.

References 

  1. Pope J, Ruthazer R, Beshansky J, Griffith J, Selker H. Clinical features of emergency department patients presenting with symptoms suggestive of acute cardiac ischemia: a multicenter study. J Thromb Thrombolys. 1998;6:63–74
  2. Ekelund U, Nilsson H-J, Frigyesi A, Torffvit O. Patients with suspected acute coronary syndrome in a university hospital emergency department: an observational study. BMC Emerg Med. 2002;2:1–7
  3. Goldman L, Cook EF, Johnson PA, Brand DA, Rouan GW, Lee TH. Prediction of the need for intensive care in patients who come to emergency departments with acute chest pain. N Engl J Med. 1996;334(23):1498–1504
  4. Selker H, Beshansky J, Griffith J, Aufderheide T, Ballin D, Bernard S,, et al. Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. a multicenter, controlled clinical trial. Ann Intern Med. 1998;129:845–855
  5. Baxt W, Shofer F, Sites F, Hollander J. A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain. Ann Emerg Med. 2002;40:575–583
  6. Xue J, Aufderheide T, Wright R, Klein J, Farrell R, Rowlandson I,, et al. Added value of new acute coronary syndrome computer algorithm for interpretation of prehospital electrocardiograms. J Electrocardiol. 2004;37:233–239
  7. Harrison R, Kennedy R. Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation. Ann Emerg Med. 2005;46:431–439
  8. Green M, Björk J, Hansen J, Ekelund U, Edenbrandt L, Ohlsson M. Detection of acute coronary syndromes in chest pain patients using neural network ensembles. In:  Fonseca JM editors. Proceedings of the second international conference on computational intelligence in medicine and healthcare. Lisbon, Portugal: IEE/IEEE; 2005;p. 182–187
  9. Kennedy R, Harrison R. Identification of patients with evolving coronary syndromes by using statistical models with data from the time of presentation. Heart. 2006;92:183–189
  10. Baxt W. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Emerg Med. 1991;115:843–848
  11. Baxt W, Skora J. Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet. 1996;347:12–15
  12. Kennedy R, Burton A, Fraser H, McStay L, Harrison R. Early diagnosis of acute myocardial infarction using clinical and electrocardiographic data at presentation: Derivation and evaluation of logistic regression models. Eur Heart J. 1996;17:1181–1191
  13. Baxt W, Shofer F, Sites F, Hollander J. A neural computational aid to the diagnosis of acute myocardial infarction. Ann Emerg Med. 2002;34:366–373
  14. Hedén B, Öhlin H, Rittner R, Edenbrandt L. Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks. Circulation. 1997;96(6):1798–1802
  15. Ohlsson M, Öhlin H, Wallerstedt S, Edenbrandt L. Usefulness of serial electrocardiograms for diagnosis of acute myocardial infarction. Am J Cardiol. 2001;88:478–481
  16. In:  Lisboa P,  Ifeachor E,  Szczepaniak P editor. Artificial neural networks in biomedicine. London: Springer-Verlag; 2000;
  17. Hansen LK, Salamon P. Neural network ensembles. IEEE Trans Pattern Anal Mach Intell. 1990;12:993–1001
  18. Krogh A, Vedelsby J. Neural network ensembles, cross-validation, and active learning. In:  Tesauro G,  Touretzky D,  Leen T editor. Advances in neural information processing systems. vol. 2:San Mateo, CA: Morgan Kaufman; 1995;p. 650–659
  19. Opitz D, Maclin R. Popular ensemble methods: An empirical study. J Artif Intell Res. 1999;11:169–198
  20. Breiman L. Bagging predictors. Mach Learn. 1996;24:123–140
  21. Niculescu-Mizil A, Caruana R. Predicting good probabilities with supervised learning. In:  Raedt LD,  Wrobel S editor. Proceedings of the 22nd international conference on machine learning. Bonn, Germany: ACM Press; 2005;
  22. Ohlsson M, Öhlin H, Wallerstedt S, Edenbrandt L. Usefulness of serial electrocardiograms for diagnosis of acute myocardial infarction. Am J Cardiol. 2001;88(5):478–481
  23. Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas A, Pajak A. Myocardial infarction and coronary deaths in the world health organization monica project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation. 1994;90:583–612
  24. Hanson SJ, Pratt LY. Comparing biases for minimal network construction with back-propagation. In:  Touretzky DS editors. Advances in neural information processing systems. vol. 1:Morgan Kaufmann; 1989;p. 177–185
  25. Dietterich TG. Ensemble methods in machine learning. Lect Notes Comput Sci. 2000;1857:1–15
  26. West D, Mangiameli P, Rampal R, West V. Ensemble strategies for a medical diagnostic decision support system: a breast cancer diagnosis application. Eur J Oper Res. 2005;162(2):532–551
  27. Hosmer D, Lemeshow S. Applied logistic regression. New York: Wiley; 1989;
  28. Lippman R, Shahian D. Coronary artery bypass risk prediction using neural networks. Ann Thorac Surg. 1997;63:1635–1643
  29. Hosmer DW, Hosmer T, le Cessie S, Lemeshow S. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med. 1997;16:965–980
  30. Hanley JA, McNeil BJ. The meaning and use of the area under the receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36
  31. Wehrens R, Putter H, Buydens L. The bootstrap: a tutorial. Chemometr Intell Lab Syst. 2000;54:35–52
  32. Pope J, Aufderheide T, Ruthazer R,, et al. Missed diagnoses of acute cardiac ischemia in the emergency department. N Engl J Med. 2000;342(16):1163–1170
  33. Karlson B, Herlitz J, Wiklund O, Richter A, Hjalmarson A. Early prediction of acute myocardial infarction from clinical history, examination and electrocardiogram in the emergency room. Am J Cardiol. 1991;68:171–175
  34. Lee T, Rouan G, Weisberg M, Brand D, Acampora D, Stasiulewicz C,, et al. Clinical characteristics and natural history of patients with acute myocardial infarction sent home from the emergency room. Am J Cardiol. 1987;60(4):219–224

PII: S0933-3657(06)00105-9

doi: 10.1016/j.artmed.2006.07.006

Artificial Intelligence in Medicine
Volume 38, Issue 3 , Pages 305-318 , November 2006