Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification

The kind of disease which causes the development of abnormal cells in any part of the body and also leads to death is called cancer. Pancreatic cancer is a type of cancer which is marked when abnormal cells start to develop in the pancreas. Sometimes, the affected individuals do not show any signs or symptoms at an early stage. There are treatments which are chosen based on how wide it has spread with the aim of extending the lives of those affected. Therefore, classification algorithms of machine learning with the best accuracy are needed to assist the medical field in classifying individuals with pancreatic cancer. In this research, classification algorithms of Decision Tree and Logistic Regression were used. Furthermore, these two methods were compared to discover which has the best performance based on accuracy. The results showed that the Decision Tree and Logistic Regression yielded 100% and 92.68% respectively as their highest accuracy. Therefore, the Decision Tree is a better method based on accuracy for classifying pancreatic cancer.