Prediction of Hepatitis C using Artificial Neural Network

The main objective of this research project is develop an expert system module, based on a back propagation feed forward artificial neural networks (ANNs), for the diagnosis of hepatitis C and compare its performance with other existing computer based decision support systems. The ANN based system was developed with a commercially available software package (Brain Maker, California scientific Software). Two different types of ANN models, unsupervised and supervised, were developed, compared, and tested. The predictive accuracy and the model training for supervised model was significantly better. The model was able to predict the Hepatitis C in patients very accurately, however performance was not significantly better than the traditional computer model based techniques. Further investigations are needed to understand the impact of this methodology on the outcome analysis. An existing database of hepatitis C infected patient was used. Data of 15 infected and 20 normal individual were collected. Dichotomous variables were coded as present (1) or not present (0). Continuous variable were recorded for patient age, ethnicity, patient number and patient sex. The results have been very interesting, however, some more research work is required to fine-tune the results. The main advantage of the developed system is that it is adaptive and self-adaptive type.