The Effect of Best First and Spreadsubsample on Selection of a Feature Wrapper With Naïve Bayes Classifier for The Classification of the Ratio of Inpatients
Abstract: Diabetes can lead to
mortality and disability, so patients should be inpatient again to undergo
treatment again to be saved. On previous research about feature selection with
greedy stepwise forward fail to predict classification ratio inpatient of
patient with the result of recall and precision 0 on data training 60%, 75%,
80%, and 90% and there is suggestion to handle unbalanced class data problem by
comparison of data readmitted 6293 and the otherwise 64141. The research
purposed to know the effect of choosing the best model using best first instead
of greedy stepwise forward and data sampling with spreadsubsample to resolve
unbalanced class data problem. The data used was patient data from 130 American
Hospital in 1999 until 2008 with 70434 data. The method that used was best
first search and spreadsubsample. The result of this research are precision
found 0.4 and 0.333 on training dataset 75% and 90% with best first method,
while spreadsubsample method found that value of precision and recall is more
significantly increased. Spreadsubsample has more effect with the result of
precision and recall rather than using best first method.
Author: M Rizky Wijaya, Ristu
Saptono, Afrizal Doewes
Journal Code: jptinformatikagg160029
