A novel machine learning approach for early detection of hepatocellular carcinoma patients. (May 2019) - Książek, Wojciech; Abdar, Moloud; Acharya, U. Rajendra; Pławiak, Paweł
Highlights: A novel method to detect hepatocellular carcinoma (HCC) is presented. Performance of ten machine learning methods are compared. Genetic algorithm (GA) is used to select the features. New 2level Genetic optimizer using stratified 5-fold cross-validation coupled with GA is employed. Obtained classification accuracy of 88.49%. Abstract: Liver cancer is quite common type of cancer among individuals worldwide. Hepatocellular carcinoma (HCC) is the malignancy of liver cancer. It has high impact on individual's life and investigating it early can decline the number of annual deaths. This study proposes a new machine learning approach to detect HCC using 165 patients. Ten well-known machine learning algorithms are employed. In the preprocessing step, the normalization approach is used. The genetic algorithm coupled with stratified 5-fold cross-validation method is applied twice, first for parameter optimization and then for feature selection. In this work, support vector machine (SVM) (type C-SVC) with new 2level genetic optimizer (genetic training) and feature selection yielded the highest accuracy and F1 -Score of 0.8849 and 0.8762 respectively. Our proposed model can be used to test the performance with huge database and aid the clinicians.