Abstract
This paper presents an application of neural networks to classify and to predict the health status of HIV/AIDS patients. A neural network model in classifying both the well and not-well health status of HIV/AIDS patients is developed and evaluated in terms of validity and reliability of the test. Several different neural network topologies are applied to AIDS Cost and Utilization Survey (ACSUS) datasets in order to demonstrate the neural network's capability.
Similar content being viewed by others
REFERENCES
Sharda, R., Neural networks for the MS/OR analyst: an application bibliography. Interfaces, 24(2):116–130, 1994.
Wilson, R. L., Ranking college football teams: a neural network approach. Interfaces 25(4):44–59, 1995.
Archer, N. P., and Wang, S., Application of the back propagation neural network algorithm with monotonicity constraints for two-group classification problems. Dec. Sci. 24(1):60–75, 1993.
Hart, A., Using neural networks for classification tasks—some experiments on datasets and practical advice, J. Operat. Res. Soc. 44:1129–1145, 1992.
Patuwo, E., Hu, M. H., and Hung, M. S., Two-group classification using neural networks. Dec. Sci. 24(4):825–845, 1993.
Wang, S., The unpredictability of standard back propagation neural networks in classification applications. Manage. Sci. 41(3):555–559, 1995.
Ebell, M. H., Artificial neural network for predicting failure to survive following in-hospital cardiopulmonary resuscitation. J. Fam. Pract. 36(3):297–303, 1993.
Faraggi, D., and Simon, R., A neural network model for survival data. Stat. Med. 14:73–82, 1995.
Baxt, W. G., Use of an artificial neural network for the diagnosis of myocardial infarction. Ann. Intern. Med. 115:843–848, 1991.
Dorffner, G., and Porenta, G., On using feedforward neural networks for clinical diagnostic tasks. Artificial Intell. Med. 6(5):417–435, 1994.
Andreassen, H., Bohr, H., Bohr, J., Brunak, S., Bugge, T., Cotterill, R. M., Jacobsen, C., Kusk, P., Lautrup, B., and Petersen, S. B., Analysis of the secondary structure of the human immunodeficiency virus (HIV) proteins p17, gp120, and gp41 by computer modeling based on neural network methods. J. Acquired Immune Def. Synd. 3(6):615–622, 1990.
Dawson, A. E., Austin, R. E., Jr., and Weinberg, D. S., Nuclear grading of breast carcinoma by image analysis: classification by multivariate and neural network analysis. Am. J. Clin. Pathol. 95(4–1):S29–37, 1991.
Floyd, C. E., Jr., Jo, J. Y., Yun, A. J., Sullivan, D. C., and Kornguth, P. J., Prediction of breast cancer malignancy using an artificial neural network. Cancer 74(11):2944–2948, 1994.
Ravdin, P. M., and Clark, G. M., A practical application of neural network analysis for predicting outcome of individual breast cancer patients. Breast Cancer Res. 22(3):285–23, 1992.
Ravdin, P. M., Clark, G. M., Hilsenbeck, S. G., Owens, M. A., Vendely, P., Pandian, M. R., and McGuire, W. L., A demonstration that breast cancer recurrence can be predicted by neural network analysis. Breast Cancer Res. Treat. 21(1):47–53, 1992.
Davis, G. E., Lowell, W. E., and Davis, G L., A neural network that predicts psychiatric length of stay. MD Comput. 10(2):87–92, 1993.
Lowell, W. E., and Davis, G. E., Predicting length of stay for psychiatric diagnosis-related groups using neural networks. J. Am. Med. Informat. Assoc. 1(6):459–466, 1994.
Tu, J. V., A comparison of neural network and logistic regression models for predicting length of stay in the intensive care unit following cardiac surgery, Unpublished Masters Thesis, University of Toronto (Canada), 1993.
Fu, L, Polygenic trait analysis by neural network learning. Art. Intell. Med. 6(1):51–65, 1994.
Kurogi, S., Speech recognition by an artificial neural network using findings on the afferent auditory system. Biological Cybernet. 64(3):243–249, 1991.
Hansen, J. V., McDonald, J. B., and Stice, J. D., Artificial intelligence and generalized qualitative-response models: an empirical test on two audit decision-making domains. Dec. Sci. 23(3):708–723, 1992.
Lenard, M. J., Alam, P., Madey, G. R., The application of neural networks and a qualitative response model to the auditor's going concern uncertainty decision. Dec. Sci. 26(2):209–227, 1995.
Malakooti, B., and Zhou, Y. Q., Feedforward artificial neural networks for solving discrete multiple criteria decision making problems. Manag. Sci. 40(11):1542–1561, 1994.
Jain, B. A., and Nag, B. N., Artificial neural network models for pricing initial public offerings, Dec. Sci. 26(3):283–302, 1995.
NueroSheel®2, Frederick, MD: Ward Systems Groups, Inc., 1993.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Kwak, N.K., Lee, C. A Neural Network Application to Classification of Health Status of HIV/AIDS Patients. Journal of Medical Systems 21, 87–97 (1997). https://doi.org/10.1023/A:1022890223449
Issue Date:
DOI: https://doi.org/10.1023/A:1022890223449