Improved non-invasive diagnosis of hepatocellular carcinoma by optimized meta classifier with hybridized features

Abstract

Hepatocellular carcinoma (HCC), the primary cancer of the liver, is life-threatening, with few or no symptoms, and detection in the early stage will help for successful treatment with surgery, and transplant for a better life quality. Here, we proposed two stacking classification models based on deep learning with differential hybrid feature selection for the early detection of HCC using novel non-invasive biomarker PIVKA-II. We showed how the variations in hybrid feature selection affect the performance of stacking classification and different supervised machine-learning algorithms on a metaclassifier. The base layers were support vector machine (SVM), gradient boosting (GB), and linear discriminant analysis (LDA). The meta classifier was a multilayer perceptron (MLP) with three different optimizers, stochastic gradient descent (SGD), adaptive moment estimation (ADAM), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Our first model outperformed the second with their hybrid features by improving accuracy by 1.5% and F1_score by 1% in both SGD and ADAM optimization, while MLP-LBFGS had a 1.4% increase in accuracy. The precision had hiked by 1.9%, 3.5%, and 1.7% in SGD, ADAM, and LBFGS, respectively, in Model-1. Matthew’s correlation coefficient (MCC) for MLP-SGD increased from 0.82 to 0.85, MLP-ADAM from 0.81 to 0.85, and MLP-LBFGS from 0.75 to 78 for the first model.