Data Mining on Patient Data

In this paper, we use machine learning schemes IR, FOIL, InductH and C5.0 to generate decision trees and rules from the examples in the medical dataset. The aim of our study is to infer the patterns that can help doctors to identify, recognize and predict the effect of the risk factors on the long term subjective cure rates of patients who undergo colposuspension. High test classification was sometimes achieved. Our best results came when one learning method suggested the preprocessing steps to be used for another method.