Volume 9, Issue 3 (9-2015)                   payavard 2015, 9(3): 224-234 | Back to browse issues page

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Samad Soltani T, Langarizadeh M, Zolnoori M. Data Mining And Analysis: Reporting Results For Patients With Asthma. payavard. 2015; 9 (3) :224-234
URL: http://payavard.tums.ac.ir/article-1-5729-en.html
1- Ph.D Student in Medical Informatics, Health Information Management Department, School of Allied Medicine, Tehran University of Medical Sciences, Tehran, Iran
2- Assistant Professor, Health Information Management Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran , Langarizadeh.m@iums.ac.ir
3- Ph.D Student in Health Informatics, Bio Health Informatics Department, School of Informatics and Computing, Indiana University, Indianapolis, USA
Abstract:   (9796 Views)
Background and Aim: Data mining is a very important branch in deeper understanding of medical data, which attempts to solve problems in the diagnosis and treatment of diseases. One of the most important data mining applications is to examine the existing data patterns. The present study aims to examine the existing data patterns of patients with asthma. Materials and Methods: This study was performed on 258 patients with respiratory symptoms, who referred to Imam Khomeini and Masih Daneshvari Hospitals in 2009. All records were entered into Excel software, and data mining add-ins were used. Analyses such as key influencers, cluster analysis of patients, and detecting exceptions have been done. Results: The most common clinical sign of asthma among subjects was severe coughing, which was highly affected by thrills. The data were aggregated into 5 clusters for more general analyses. Their common denominator was then identified and the records with exceptional features were determined. Then, following cost analysis and setting the threshold value at 612, a questionnaire was developed based on data features for diagnosis of asthma. Conclusion: The developed framework for data mining and analysis is an appropriate tool for knowledge extraction based on the data and their relationships. Meanwhile, it can identify and fill the existing gap in medical decision- making when using clinical guideline
Full-Text [PDF 259 kb]   (5751 Downloads)    
Type of Study: Research | Subject: Hospital Managment
ePublished: 1399/07/23

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