An Integrated Neural Network and Evolutionary Algorithm Approach for Liver Fibrosis Staging: Can Artificial Intelligence Reduce Patient Costs?

Background: Liver fibrosis is important in terms of staging, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C. Method: We proposed a method based on a selection of machine learning classification methods including Multi Layers Perceptron neural network (MLP), Naive Bayesian (NB), decision tree, and deep learning. Initially, the Synthetic minority oversampling technique (SMOTE) was performed to address the imbalance of the dataset. Afterward, the integration of MLP and TLBO was implemented. Result: We proposed a novel algorithm that reduced the number of required patient features to 7 inputs. The accuracy of MLP using 12 features is 0.903, while the accuracy of the proposed MLP with the TLBO method is 0.891. Besides, the diagnostic accuracy in all methods, except the model designed with the Bayesian Network, increased when the SMOTE balancer was applied. Conclusion: The Decision tree deep learning methods showed the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and 7 features, the MLP reached a 0.891 accuracy rate which is quite satisfying compared with similar studies. The proposed model provided great diagnostic accuracy by reducing the required properties from the samples without reducing the accuracy. The results of our study showed that the recruited algorithm of our study was more straightforward, with lower required properties and similar accuracy.