Authors
1 Assistant Proffessor, Economics, The University of Isfahan, Isfahan, Iran
2 MSc, Development and Planning Economics, The University of Isfahan, Isfahan, Iran
Abstract
Introduction: Using neural networks and genetic algorithms in evaluating health-related
variables has increased recently. Employing intelligent tools for diagnosis and treatment of
diseases can reduce medical errors and human and financial losses. In this paper, medical
applications of neural networks have been studied in order to help both medical and artificial
intelligence researchers.
Methods: We used an existing sample in SPSS (patient_los.sav). The sample consisted of
patients who received treatment for heart disease. Multilayer perceptron (MLP) was employed to
build a neural network to predict the cost and length of treatment. Duration of hospitalization and
treatment cost were considered as dependent variables. Other variables were entered into the
model as agents or factors.
Results: Neural networks can evaluate the outcomes of patients who have or have not undergone
surgery. Separate networks can then be used to predict treatment and hospitalization costs and
duration provided that the patients who had surgery had been identified.
Conclusion: Neural networks designed in this paper can well forecast the usual outcomes of
patients. Multilayer neural networks can precisely identify patients who would die after surgery.
Non-linear properties of neural networks can help in modeling and forecasting.
Keywords
)