  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.
