Nazarizadeh Ali, Banirostam Touraj, Biglari Taraneh, Kalantarhormozi Mohammadreza, Chichagi Fatemeh, Behnoush Amir H, Habibi Mohammad A, Shahidi Ramin
Department of Computer Engineering Central Tehran Branch, Islamic Azad University Tehran Iran.
The Persian Gulf Tropical Medicine Research Center The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences Bushehr Iran.
JGH Open. 2024 May 9;8(5):e13075. doi: 10.1002/jgh3.13075. eCollection 2024 May.
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.
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.
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.
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.
肝纤维化分期很重要，肝活检是金标准诊断工具。我们旨在利用基于教学学习的优化（TLBO）算法设计并评估一种人工神经网络（ANN）方法，用于预测献血者和丙型肝炎患者的肝纤维化分期。
我们提出一种基于多种机器学习分类方法的方法，包括多层感知器（MLP）神经网络、朴素贝叶斯（NB）、决策树和深度学习。最初，执行合成少数过采样技术（SMOTE）以解决数据集中的不平衡问题。之后，实现MLP与TLBO的整合。
我们提出一种新颖的算法，将所需患者特征数量减少到七个输入。使用12个特征的MLP的准确率为0.903，而所提出的带有TLBO的MLP的准确率为0.891。此外，当应用SMOTE平衡器时，除了基于贝叶斯网络设计的模型外，所有方法的诊断准确率均有所提高。
基于决策树的深度学习方法在使用12个特征时显示出最高的准确率。有趣的是，通过使用TLBO和七个特征，MLP达到了0.891的准确率，与类似研究相比相当令人满意。所提出的模型提供了高诊断准确率，同时减少了样本所需的属性数量。我们的研究结果表明，我们研究中所采用的算法更直接，所需属性数量更少且准确率相似。
