Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

• Published in: JGH open Vol. 8; no. 5; pp. e13075 - n/a
• Main Authors: Nazarizadeh, Ali, Banirostam, Touraj, Biglari, Taraneh, Kalantarhormozi, Mohammadreza, Chichagi, Fatemeh, Behnoush, Amir H, Habibi, Mohammad A, Shahidi, Ramin
• Format: Journal Article
• Language: English
• Published: Melbourne Wiley Publishing Asia Pty Ltd 01.05.2024; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Artificial intelligence; Biopsy; Blood & organ donations; cirrhosis; Data mining; Datasets; Decision making; Decision trees; Deep learning; diagnostic accuracy; hepatic disease; hepatitis; Hepatitis C; hepatology; Liver cancer; Liver cirrhosis; Machine learning; Neural networks; Optimization algorithms; Original; Teaching; hepatology; diagnostic accuracy; hepatitis; artificial intelligence; cirrhosis; hepatic disease
• ISSN: 2397-9070; 2397-9070
• DOI: 10.1002/jgh3.13075

Background and Aim Staging liver fibrosis is important, 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 patients. Methods We propose a method based on a selection of machine learning classification methods including multilayer perceptron (MLP) neural network, Naive Bayesian (NB), decision tree, and deep learning. Initially, the synthetic minority oversampling technique (SMOTE) is performed to address the imbalance in the dataset. Afterward, the integration of MLP and TLBO is implemented. Results We propose a novel algorithm that reduces the number of required patient features to seven inputs. The accuracy of MLP using 12 features is 0.903, while that of the proposed MLP with TLBO is 0.891. Besides, the diagnostic accuracy of all methods, except the model designed with the Bayesian network, increases when the SMOTE balancer is applied. Conclusion The decision tree‐based deep learning methods show the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and seven features, MLP reached an accuracy rate of 0.891, which is quite satisfactory when compared with those of similar studies. The proposed model provides high diagnostic accuracy, while reducing the required number of properties from the samples. The results of our study show that the recruited algorithm of our study is more straightforward, with a smaller number of required properties and similar accuracy. An artificial neural network (ANN) method, leveraging the Teaching Learning‐Based Optimization (TLBO) algorithm, is designed and evaluated for predicting liver fibrosis stage in blood donors and hepatitis C patients. By incorporating a selection of machine learning classification methods and utilizing only seven patient features, we could achieve a promising accuracy rate of 0.891, demonstrating the efficiency and diagnostic accuracy of the proposed model.