Background and Objective

Considered as one of the most recurrent types of liver malignancy, Hepatocellular Carcinoma (HCC) needs to be assessed in a non-invasive way. The objective of the current study is to develop prediction models for Chronic Hepatitis C (CHC)-related HCC using machine learning techniques.

Methods

A dataset, for 4423 CHC patients, was investigated to identify the significant parameters for predicting HCC presence. In this study, several machine learning techniques (Classification and regression tree, alternating decision tree, reduce pruning error tree and linear regression algorithm) were used to build HCC classification models for prediction of HCC presence.

Results

Age, alpha-fetoprotein (AFP), alkaline phosphate (ALP), albumin, and total bilirubin attributes were statistically found to be associated with HCC presence. Several HCC classification models were constructed using several machine learning algorithms. The proposed HCC classification models provide adequate area under the receiver operating characteristic curve (AUROC) and high accuracy of HCC diagnosis. AUROC ranges between 95.5% and 99%, plus overall accuracy between 93.2% and 95.6%.

Conclusion

Models with simplistic factors have the power to predict the existence of HCC with outstanding performance.

中文翻译：

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诊断与HCV相关的慢性肝病的肝细胞癌的机器学习预测模型。

背景与目的

肝细胞癌（HCC）被认为是最常见的肝恶性肿瘤之一，需要以非侵入性方式进行评估。本研究的目的是使用机器学习技术为慢性丙型肝炎（CHC）相关的HCC开发预测模型。

方法

研究了一个针对4423名CHC患者的数据集，以确定用于预测HCC存在的重要参数。在这项研究中，使用了几种机器学习技术（分类和回归树，交替决策树，减少修剪错误树和线性回归算法）来建立HCC分类模型，以预测HCC的存在。

结果

从统计学上发现，年龄，甲胎蛋白（AFP），碱性磷酸酶（ALP），白蛋白和总胆红素属性与HCC的存在有关。使用多种机器学习算法构建了多种HCC分类模型。提出的HCC分类模型在接收器工作特性曲线（AUROC）下提供了足够的区域，并提供了HCC诊断的高精度。AUROC的范围在95.5％至99％之间，加上整体准确度在93.2％至95.6％之间。

结论

具有简单因素的模型可以预测具有卓越性能的HCC的存在。