Assessment of HIV/AIDS-related health performance using an artificial neural network

Abstract This paper presents an application of neural networks to classify and predict the symptomatic status of HIV/AIDS patients. The purpose of this study is to apply an artificial neural network (ANN) to provide correct classification of AIDS versus HIV status patients. An ANN model is developed using publicly available HIV/AIDS data in the AIDS Cost and Services Utilization Survey (ACSUS) datasets as input and output variables. The proposed model: 1. demonstrates which factors will affect classification of AIDS and HIV status; 2. reinforces HIV/AIDS patient prevention and care planning and strategies to meet more appropriately health-care policy and regulations; 3. provides decision-makers and policy-makers with more accurate information to allow them to implement better health-care systems. Several different neural network topologies are applied to the datasets. A neural network model was developed to classify both the HIV and AIDS status of patients and analyzed in terms of validity and reliability of the test in order to demonstrate the model capability. The ANN model can facilitate planning, decision-making, and managerial control by providing hospital administration information.

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