Disease detection in medical prescriptions using data mining tools

Author

Alamdari, Mahsa Soudi ; Teimouri, Mehdi ; Farzadfar, Farshad ; Hashemi-Meshkini, Amir

Author_Institution

Dept. of Network Sci. & Technol., Univ. of Tehran, Tehran, Iran

fYear

2014

fDate

29-30 Oct. 2014

Firstpage

159

Lastpage

164

Abstract

Prevalence of communicable and non-communicable diseases is one of the most important categories of epidemiological data that is used for interpreting health status of communities. This study is aimed to calculate the prevalence of outpatient diseases through characterization of outpatient prescriptions. The data used in this study is collected from 1412 prescriptions of various diseases and we have focused on the identification of ten diseases. In this study data mining tools is used to identify diseases related to each prescription. Then we have compared the performance of these methods with a Naïve method. The results indicate that implementation of data mining algorithms has a good performance in characterization of outpatient diseases. These results can help to choose the appropriate method for classification of prescriptions in larger scales.

Keywords

data mining; diseases; medical administrative data processing; medical computing; Naive method; data mining tools; disease detection; epidemiological data; medical prescriptions; noncommunicable diseases; outpatient diseases; Accuracy; Data mining; Diseases; Drugs; Logistics; Medical diagnostic imaging; Support vector machines; Naïve method; data mining; diagnosis; medical prescription; outpatient diseases;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Knowledge Engineering (ICCKE), 2014 4th International eConference on

Conference_Location

Mashhad

Print_ISBN

978-1-4799-5486-5

Type

conf

DOI

10.1109/ICCKE.2014.6993357

Filename

6993357