Comparative Study of J48 Decision Tree and CART Algorithm for Liver Cancer Symptom Analysis Using Data from Carnegie Mellon University

Abstract

Liver cancer is a major contributor to cancer-related mortality both in the United States and worldwide. A range of liver diseases, such as chronic liver disease, liver cirrhosis, hepatitis, and liver cancer, play a role in this statistic. Hepatitis, in particular, is the main culprit behind liver cancer. As a consequence, it is decisive to investigate the correlation between hepatitis and symptoms using statistic inspection. In this study, we inspect 155 patient data possessed by CARNEGIE-MELLON UNIVERSITY in 1988 to prognosticate whether an individual died from liver disease using supervised machine learning models for category and connection rules based on 20 different symptom attributes. We compare J48 (Gain Ratio) and CART (Classification and Regression Tree), two decision tree classification algorithms elaborate from ID3 (Iterative Dichotomiser 3), with the Gini index in a Java environment. The data is preprocessed through normalization. Our study demonstrates that J48 outperforms CART, with an average accuracy rate of nearly 87% for the complete specimen, cross-validation, and 66% training data. However, CART has the supreme accurate rate in all samples, with an accuracy rate of 90.3232%. Furthermore, our research indicates that removing the conjunction attribute of the Apriori algorithm does not impact the results. This research showcases the potential for physician and researchers to apply brief machine learning device to attain accurate outcomes and develop treatments based on symptoms.