Comparison of Two Data Mining Techniques in Labeling Diagnosis to Iranian Pharmacy Claim Dataset: $UWLøFLDO1HXUDO1HWZRUN$119HUVXV'HFLVLRQ7UHH0RGHO

Background: This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. Methods: This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician