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Diagnosis of MRSA with neural networks and logistic regression approach

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  • Jen Shang
  • Yu-sen Lin
  • Angella Goetz

Abstract

Antibiotic-resistant pathogens are increasingly prevalent in the hospitals and community. A timely and accurate diagnosis of the infection would greatly help physicians effectively treat patients. In this research we investigate the potential of using neural networks (NN) and logistic regression (LR) approach in diagnosing methicillin-resistant Staphylococcus aureus (MRSA). Receiver-Operating Characteristic (ROC) curve and the cross-validation method are used to compare the performances of both systems. We found that NN is better than the logistic regression approach, in terms of both the discriminatory power and the robustness. With modeling flexibility inherent in its techniques, NN is effective in dealing with MRSA and other classification problems involving large numbers of variables and interaction complexity. On the other hand, logistic regression in our case is slightly inferior, offers more clarity and less perplexity. It could be a method of choice when fewer variables are involved and/or justification of the results is desired. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Jen Shang & Yu-sen Lin & Angella Goetz, 2000. "Diagnosis of MRSA with neural networks and logistic regression approach," Health Care Management Science, Springer, vol. 3(4), pages 287-297, September.
    • Handle: RePEc:kap:hcarem:v:3:y:2000:i:4:p:287-297
    DOI: 10.1023/A:1019018129822
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    Cited by:

    1. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    2. De Angelis, Vanda & Felici, Giovanni & Impelluso, Paolo, 2003. "Integrating simulation and optimisation in health care centre management," European Journal of Operational Research, Elsevier, vol. 150(1), pages 101-114, October.

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