Liver Cancer Patient Classification on a Multiple-Stage using Hybrid Classification Methods

Abstract

This paper proposes a model to detect liver cancer patients and estimate the abnormality level of livers using a classification method based on an Indian liver patient dataset. The dataset is prepared by 3 processes: preliminary study, data cleansing, and handling imbalanced class to build the model based on multiple-stages using hybrid classification methods. The 1st stage is liver cancer patient detection. The 2nd stage is abnormality level of liver estimation, as divided using the DeRitis Ratio. The abnormality level of livers is divided into 3 levels: low, medium, and high, called ALL framework. Machine learning method is used to build multiple classification stages, which consist of Multilayer Perceptron, Logistic Regression, and Random Forest. The experimental results demonstrate that the 1st model (stage I) can detect liver cancer patient with 78.88 % accuracy. The 2nd model (stage II) achieves accuracy of 99.83 % for abnormality level of liver estimation. In addition, we compare our proposed model with another dataset. Our proposed model also outperforms detection with 76.73 and 98.26 % accuracy in stage I and stage II, respectively. Our proposed model is a benefit for physicians to support diagnosis and treatment, especially in the case of physicians desiring an intelligent decision support system.