Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining
ObjectivesThe purpose of this study was to analyze the records of patients diagnosed with essential hypertension using association rule mining (ARM).MethodsPatients with essential hypertension (ICD code, I10) were extracted from a hospital's data warehouse and a data mart constructed for analys...
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| Format: | Article |
| Language: | English |
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The Korean Society of Medical Informatics
2010-06-01
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| Series: | Healthcare Informatics Research |
| Subjects: | |
| Online Access: | http://e-hir.org/upload/pdf/hir-16-77.pdf |
| _version_ | 1828137672916336640 |
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| author | A Mi Shin In Hee Lee Gyeong Ho Lee Hee Joon Park Hyung Seop Park Kyung Il Yoon Jung Jeung Lee Yoon Nyun Kim |
| author_facet | A Mi Shin In Hee Lee Gyeong Ho Lee Hee Joon Park Hyung Seop Park Kyung Il Yoon Jung Jeung Lee Yoon Nyun Kim |
| author_sort | A Mi Shin |
| collection | DOAJ |
| description | ObjectivesThe purpose of this study was to analyze the records of patients diagnosed with essential hypertension using association rule mining (ARM).MethodsPatients with essential hypertension (ICD code, I10) were extracted from a hospital's data warehouse and a data mart constructed for analysis. Apriori modeling of the ARM method and web node in the Clementine 12.0 program were used to analyze patient data.ResultsPatients diagnosed with essential hypertension totaled 5,022 and the diagnostic data extracted from those patients numbered 53,994. As a result of the web node, essential hypertension, non-insulin dependent diabetes mellitus (NIDDM), and cerebral infarction were shown to be associated. Based on the results of ARM, NIDDM (support, 35.15%; confidence, 100%) and cerebral infarction (support, 21.21%; confidence, 100%) were determined to be important diseases associated with essential hypertension.ConclusionsEssential hypertension was strongly associated with NIDDM and cerebral infarction. This study demonstrated the practicality of ARM in co-morbidity studies using a large clinic database. |
| first_indexed | 2024-04-11T18:23:42Z |
| format | Article |
| id | doaj.art-db50c273d5344eb8afc868b678a09bf5 |
| institution | Directory Open Access Journal |
| issn | 2093-3681 2093-369X |
| language | English |
| last_indexed | 2024-04-11T18:23:42Z |
| publishDate | 2010-06-01 |
| publisher | The Korean Society of Medical Informatics |
| record_format | Article |
| series | Healthcare Informatics Research |
| spelling | doaj.art-db50c273d5344eb8afc868b678a09bf52022-12-22T04:09:43ZengThe Korean Society of Medical InformaticsHealthcare Informatics Research2093-36812093-369X2010-06-01162778110.4258/hir.2010.16.2.77578Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule MiningA Mi Shin0In Hee Lee1Gyeong Ho Lee2Hee Joon Park3Hyung Seop Park4Kyung Il Yoon5Jung Jeung Lee6Yoon Nyun Kim7Department of Medical Informatics, School of Medicine, Keimyung University, Daegu, Korea.Department of Medical Informatics, School of Medicine, Keimyung University, Daegu, Korea.Department of Medical Informatics, School of Medicine, Keimyung University, Daegu, Korea.Department of Medical Informatics, School of Medicine, Keimyung University, Daegu, Korea.Department of Internal Medicine, School of Medicine, Keimyung University, Daegu, Korea.Department of Medical Informatics, School of Medicine, Keimyung University, Daegu, Korea.Department of Preventive Medicine, School of Medicine, Keimyung University, Daegu, Korea.Department of Medical Informatics, School of Medicine, Keimyung University, Daegu, Korea.ObjectivesThe purpose of this study was to analyze the records of patients diagnosed with essential hypertension using association rule mining (ARM).MethodsPatients with essential hypertension (ICD code, I10) were extracted from a hospital's data warehouse and a data mart constructed for analysis. Apriori modeling of the ARM method and web node in the Clementine 12.0 program were used to analyze patient data.ResultsPatients diagnosed with essential hypertension totaled 5,022 and the diagnostic data extracted from those patients numbered 53,994. As a result of the web node, essential hypertension, non-insulin dependent diabetes mellitus (NIDDM), and cerebral infarction were shown to be associated. Based on the results of ARM, NIDDM (support, 35.15%; confidence, 100%) and cerebral infarction (support, 21.21%; confidence, 100%) were determined to be important diseases associated with essential hypertension.ConclusionsEssential hypertension was strongly associated with NIDDM and cerebral infarction. This study demonstrated the practicality of ARM in co-morbidity studies using a large clinic database.http://e-hir.org/upload/pdf/hir-16-77.pdfhypertensiondiagnosisdata mining |
| spellingShingle | A Mi Shin In Hee Lee Gyeong Ho Lee Hee Joon Park Hyung Seop Park Kyung Il Yoon Jung Jeung Lee Yoon Nyun Kim Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining Healthcare Informatics Research hypertension diagnosis data mining |
| title | Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining |
| title_full | Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining |
| title_fullStr | Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining |
| title_full_unstemmed | Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining |
| title_short | Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining |
| title_sort | diagnostic analysis of patients with essential hypertension using association rule mining |
| topic | hypertension diagnosis data mining |
| url | http://e-hir.org/upload/pdf/hir-16-77.pdf |
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