A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

Abstract

The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score.

Improving hepatocellular carcinoma diagnosis using an ensemble classification approach based on Harris Hawks Optimization

“…We use Ada-boost, naive bayes, random forest, ID3, logistic regression, support vector machine, and k-nearest neighbor models as classical classifiers [ 40 ], as well as use ECSL [ 19 ], RFE-GB [ 21 ], LASSO [ 21 ], DTPSO [ 25 ] and ELCM [ 26 ] methods as ensemble classifiers for comparison work. We use criteria such as accuracy, precision, recall and F1-score for evaluation and validation [ 41 ].…”

“…In the last experiment, we evaluate the proposed ensemble classification method in comparison with some equivalent methods. These methods include ECSL [ 19 ], RFE-GB [ 21 ], LASSO [ 21 ], DTPSO [ 25 ] and ELCM [ 26 ]. Table 3 shows the results of this comparison on the HCC dataset based on accuracy, precision, recall and F1-score criteria.…”

“…Ali et al [ 21 ] proposed the Recursive Feature Elimination with Gradient Boosting (RFE-GB) method to predict liver cancer survival. RFE-GB is applied on the HCC dataset, where missing values are filled based on three strategies including mean, mode, and KNN.…”

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