Author/Authors :
Teimouri, Mehdi Department of Network Science and Technology - Faculty of New Sciences and Technologies - University of Tehran, Tehran , Soudi Alamdari, Mahsa Department of Network Science and Technology - Faculty of New Sciences and Technologies - University of Tehran, Tehran , Farzadfar, Farshad Non-communicable disease Research Center - Endocrinology and Metabolism Population Science Institute, Tehran University of Medical Sciences, Tehran , Hashemi-Meshkini, Amir Non-communicable disease Research Center - Endocrinology and Metabolism Population Science Institute, Tehran University of Medical Sciences, Tehran , Rezaei-Darzi, Ehsan Non-communicable disease Research Center - Endocrinology and Metabolism Population Science Institute, Tehran University of Medical Sciences, Tehran , Varmaghani, Mehdi Non-communicable disease Research Center - Endocrinology and Metabolism Population Science Institute, Tehran University of Medical Sciences, Tehran , Adibi Alamdari, Parisa School of medicine - Shahid Beheshti University of Medical Sciences, Tehran , Zeynalabedini, Aysan School of Medicine, Orumia University of Medical Sciences, Orumia
Abstract :
Data about the prevalence of communicable and non-communicable diseases, as one of
the most important categories of epidemiological data, is used for interpreting health status of
communities. This study aims to calculate the prevalence of outpatient diseases through the
characterization of outpatient prescriptions. The data used in this study is collected from 1412
prescriptions for various types of diseases from which we have focused on the identification of
ten diseases. In this study, data mining tools are used to identify diseases for which prescriptions
are written. In order to evaluate the performances of these methods, we compare the results
with Naïve method. Then, combining methods are used to improve the results. Results showed
that Support Vector Machine, with an accuracy of 95.32%, shows better performance than the
other methods. The result of Naive method, with an accuracy of 67.71%, is 20% worse than
Nearest Neighbor method which has the lowest level of accuracy among the other classification
algorithms. The results indicate that the implementation of data mining algorithms resulted in
a good performance in characterization of outpatient diseases. These results can help to choose
appropriate methods for the classification of prescriptions in larger scales
Keywords :
Outpatient Diseases , Medical Prescription , Diagnosis , Data Mining , Voting , Weighted Voting , Stacking
