A neural network application to classification of health status of HIV/AIDS patients.
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.
Kwak
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Saint Louis University, School of Business and Administration, Dept. of Decision Sciences and MIS, MO 63108, USA.
Lee
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D000203:Activities of Daily Living; D003936:Diagnosis, Computer-Assisted; D015658:HIV Infections / Q000145:classification*; D017048:Health Care Costs; D006296:Health Services / Q000706:statistics & numerical data; D006302:Health Services Research; D006304:Health Status; D006801:Humans; D016571:Neural Networks, Computer; D012044:Regression Analysis; D015203:Reproducibility of Results