Comparison and Evaluation of Decision Tree Algorithms using Medical Records

C Sasikala

Abstract


In Image processing, classification is the important technique used to extract models describing various data classes. Classification is a predictive model has applications in Machine learning, Expert Systems, Statistics and Medical diagnosis. Classification is used in to make betterment and efficiency in medical applications for data analysis and decision making process. Decision tree is the most significant classifier to diagnose patient medical problems in terms to diagnose breast cancer, ovarian cancer, heart sound diagnosis, vocal fold diseases and fetal growth. Decision tree is constructed to model the classification process. In this paper Decision tree construction and important algorithms working procedures are discussed using ID3, C4.5 and CART using medical applications. Then performance of algorithms is evaluated using test data with various sizes. Performance evaluation of algorithms is done using the parameters as size of the data, accuracy and time complexity. Finally major issues of the Decision tree are focused.

Keywords


Predictive model, ID3, C4.5, CART, Accuracy and Time complexity.

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