Volume 9, Issue 3 (9-2015)
payavard 2015, 9(3): 224-234 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
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
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
2- Assistant Professor, Health Information Management Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran ,
3- Ph.D Student in Health Informatics, Bio Health Informatics Department, School of Informatics and Computing, Indiana University, Indianapolis, USA
Abstract: (11116 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
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
