COMPARISON OF DECISION TREE METHODS FOR BREAST CANCER DIAGNOSIS
In almost all parts of the world, breast cancer is one of the major causes of death among women. But at the same time, it is one of the most curable cancers if it is diagnosed at early stage. This paper tries to find a model that diagnoses and classifies breast cancer with high accuracy and that will help to both patients and doctors in the future. Here we present several different decision tree methods in order to classify breast cancer with high accuracy. The results achieved in this research are very promising (accuracy is 96.49 %). It is very promising result compared to previous researches where decision tree techniques were used. As benchmark test, Breast Cancer Wisconsin (Original) was used.