Comparison of Naive Bayes and Decision Tree for Classifying Hepatocellular Carcinoma (HCC)

Cancer is a disease that causes abnormal cell growth in the body. An example is a liver cancer and it has several types. One of which is Hepatocellular Carcinoma (HCC), and it is the most common one. HCC usually affects people with cirrhosis and hepatitis B or C. Affected people sometimes do not show any specific signs or symptoms at an early stage, and it is usually diagnosed when it has reached a critical stage. Therefore, accurate classification is needed in helping the medical field to classify people with HCC. The research aims to classify HCC patients using supervised machine learning. The HCC dataset from Al-Islam Hospital, Bandung, Indonesia was classified using Naive Bayes and Decision Tree. Both of these methods were compared to determine which one worked best in terms of accuracy. The result showed that Naive Bayes and Decision Tree achieved the best accuracy at 98.25% and 100% respectively. Considering this result, it is reasonable to conclude that Decision Tree performs better in accuracy for HCC classification.