CRDT: Correlation Ratio Based Decision Tree Model for Healthcare Data Mining

Roy, S and Mondal, S and Ekbal, A and Desarkar, Maunendra Sankar (2016) CRDT: Correlation Ratio Based Decision Tree Model for Healthcare Data Mining. In: 16TH International Conference on Bioinformatics and Bioengineering (BIBE), OCT 31-NOV 02, 2016, Taichung, TAIWAN.

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The phenomenal growth in the healthcare data has inspired us in investigating robust and scalable models for data mining. For classification problems Information Gain(IG) based Decision Tree is one of the popular choices. However, depending upon the nature of the dataset, IG based Decision Tree may not always perform well as it prefers the attribute with more number of distinct values as the splitting attribute. Healthcare datasets generally have many attributes and each attribute generally has many distinct values. In this paper, we have tried to focus on this characteristics of the datasets while analysing the performance of our proposed approach which is a variant of Decision Tree model and uses the concept of Correlation Ratio(CR). Unlike IG based approach, this CR based approach has no biasness towards the attribute with more number of distinct values. We have applied our model on some benchmark healthcare datasets to show the effectiveness of the proposed technique.

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IITH Creators:
IITH CreatorsORCiD
Desarkar, Maunendra SankarUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Data Mining; Healthcare; Decision Tree; Information Gain; Correlation Ratio
Subjects: Computer science > Computer programming, programs, data
Computer science > Special computer methods
Others > Medicine
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 09 Feb 2017 07:08
Last Modified: 01 Sep 2017 11:29
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