A Random Forest - Genetic algorithm integration approach for Hepatocellular Carcinoma Early prediction

Abstract

Malignant liver cancer is Hepatocellular carcinoma (HCC). It has a strong effect on individuals’ lives and may decrease the number of annual deaths by early investigating.There are liver disease forms, including fatty liver, cirrhosis, hepatitis, chronic liver disease, liver cancer, and hepatocellular carcinoma (HCC). As the incidence of this disease has risen dramatically in recent years, several researchers have also devoted great attention to this issue through expert systems and machine learning. Machine learning has been applied more recently to cancer prognosis and prediction. In this work, machine learning methods based on a k-nearest neighbor, support vector machine, naïve Bayes, and random forest classifiers with the integration of genetic algorithm for classifying 165 liver cancer patients with specific 49 combinations of features, testing all classifiers by assessing their output based on accuracy, error rate, sensitivity, prevalence, and specificity. Results show that the random forest with parameters optimization using genetic algorithm (GA) yielded the highest accuracy, and specificity indicating improvement over the classical approaches commonly used in predictive models of Hepatocellular Carcinoma. The finding referred to various useful attributes such as alpha-fetoprotein, hemoglobin, alkaline phosphatase, total bilirubin, albumin, and total proteins, which can help in early prediction of hepatocellular carcinoma.