Prediction of hepatocellular carcinoma using a machine learning algorithm

Abstract

The prevention of hepatocellular carcinoma (HCC), which is rated third for causing death due to cancer in the world, and the selection of more effective treatment have necessitated the development of HCC diagnosis and prediction systems using artificial intelligence. The presented paper examines the possibility of applying machine learning algorithms to predict liver cancer. Machine learning methods such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) are used to predict HCC. The HCC Dataset taken from the website Kaggle (Kaggle.com) is referenced for the realization of prediction. This research uses the libraries scikit- learn, Pandas, NumPy, etc. in the Jupiter programming environment to conduct experiments. The results of the experiments are compared, and the RF classifier is estimated to perform the highest result. Referring to this fact, the importance of using the RF method in building an initial HCC diagnosis and prognosis system is justified.