An Efficient Approach of Decision Tree for Classifying Brain Tumors

Decision Trees are considered to be one of the most popular approaches for representing classifiers. Statistics, machine learning, pattern recognition, and Data Mining have dealt with the issue of growing a decision tree from available data. The purpose of this work is to present an updated survey of current methods for constructing decision tree for classifying brain tumours. The main focus is on solving the cancer classification problem using single decision tree classifiers (CART and Random algorithm) showing strengths and weaknesses of the proposed methodologies when compared to other popular classification methods. This paper presents a literature review of articles related to the use of decision tree classifiers which classifies brain tumours into main categories.