Weighted Synthetic Minority Over-Sampling Technique (WSMOTE) Algorithm and Ensemble Classifier for Hepatocellular Carcinoma (HCC) In Liver Disease System

Abstract

HCC (Hepato Cellular Carcinoma) is a generic liver cancer causing death in people suffering from LC (Liver Cirrhosis). Early prognosis of HCC is a significant factor to the line of LC treatment for clinicians. Though current treatments for HCC have been effective, patients have responded negatively or exhibited aggressive biological behaviours. Identification accuracy reduces when sub-optimal models of MLTs (Machine Learning Techniques) process multiple classes. MLT models base their predictions on their training phases where class imbalances in datasets have retained challenges in terms of unsatisfactory results. This research work attempts to overcome these issues with its proposed WSMOTE (Weighted Synthetic Minority Over-sampling Technique) algorithm which is targeted at dataset imbalances. Missing values are imputed using IFCM (Improved Fuzzy C Means) clustering for enhancing analysis accuracy. Imputed features are chosen selectively using IEFS (Intelligence Ensemble-Based Feature Selection). A heterogeneous ensemble classifier using Bootstrap aggregation is applied for combining predictions of multiple classifiers including KSVMs (Kernel Support Vector Machines) and FCNNs (Fuzzy Convolution Neural Networks) for accuracy in outputs. This work’s schema when tested on MATLAB (Matrix Laboratory) was found to classify better than most other methods in terms of precision, recall, F-measure and accuracy.