Exploitation of Clustering Techniques in Transactional Healthcare Data
Healthcare service centres equipped with electronic health systems have improved their resources as well as treatment processes. The dynamic nature of healthcare data of each individual makes it complex and difficult for physicians to manually mediate them; therefore, automatic techniques are essent...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Mehran University of Engineering and Technology
2014-03-01
|
Series: | Mehran University Research Journal of Engineering and Technology |
Subjects: | |
Online Access: | http://publications.muet.edu.pk/research_papers/pdf/pdf845.pdf |
id |
doaj-5ed457d2db484bbe995d4b757ef9d4c9 |
---|---|
record_format |
Article |
spelling |
doaj-5ed457d2db484bbe995d4b757ef9d4c92020-11-24T23:48:55ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192014-03-013317792Exploitation of Clustering Techniques in Transactional Healthcare DataNaeem Ahmad Mahoto0Faisal Karim Shaikh1Abdul Qadir Ansari2Department of Sotware Engineering, Mehran University of Engineering & Technology, Jamshoro, PakistanDepartment of Telecommunication Engineering, Mehran University of Engineering & Technology, Jamshoro, PakistanSenior Manager, Multimedia and Broadband, Pakistan Telecommunication Company Limited, Hyderabad, PakistanHealthcare service centres equipped with electronic health systems have improved their resources as well as treatment processes. The dynamic nature of healthcare data of each individual makes it complex and difficult for physicians to manually mediate them; therefore, automatic techniques are essential to manage the quality and standardization of treatment procedures. Exploratory data analysis, patternanalysis and grouping of data is managed using clustering techniques, which work as an unsupervised classification. A number of healthcare applications are developed that use several data mining techniques for classification, clustering and extracting useful information from healthcare data. The challenging issue in this domain is to select adequate data mining algorithm for optimal results. This paper exploits three different clustering algorithms: DBSCAN (Density-Based Clustering), agglomerative hierarchical and k-means in real transactional healthcare data of diabetic patients (taken as case study) to analyse their performance in large and dispersed healthcare data. The best solution of cluster sets among the exploited algorithms is evaluated using clustering quality indexes and is selected to identify the possible subgroups of patients having similar treatment patternshttp://publications.muet.edu.pk/research_papers/pdf/pdf845.pdfClustering TechniquesData MiningHealthcare ApplicationsDiabetes |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Naeem Ahmad Mahoto Faisal Karim Shaikh Abdul Qadir Ansari |
spellingShingle |
Naeem Ahmad Mahoto Faisal Karim Shaikh Abdul Qadir Ansari Exploitation of Clustering Techniques in Transactional Healthcare Data Mehran University Research Journal of Engineering and Technology Clustering Techniques Data Mining Healthcare Applications Diabetes |
author_facet |
Naeem Ahmad Mahoto Faisal Karim Shaikh Abdul Qadir Ansari |
author_sort |
Naeem Ahmad Mahoto |
title |
Exploitation of Clustering Techniques in Transactional Healthcare Data |
title_short |
Exploitation of Clustering Techniques in Transactional Healthcare Data |
title_full |
Exploitation of Clustering Techniques in Transactional Healthcare Data |
title_fullStr |
Exploitation of Clustering Techniques in Transactional Healthcare Data |
title_full_unstemmed |
Exploitation of Clustering Techniques in Transactional Healthcare Data |
title_sort |
exploitation of clustering techniques in transactional healthcare data |
publisher |
Mehran University of Engineering and Technology |
series |
Mehran University Research Journal of Engineering and Technology |
issn |
0254-7821 2413-7219 |
publishDate |
2014-03-01 |
description |
Healthcare service centres equipped with electronic health systems have improved their resources as well as treatment processes. The dynamic nature of healthcare data of each individual makes it complex and difficult for physicians to manually mediate them; therefore, automatic techniques are essential to manage the quality and standardization of treatment procedures. Exploratory data analysis, patternanalysis and grouping of data is managed using clustering techniques, which work as an unsupervised classification. A number of healthcare applications are developed that use several data mining techniques for classification, clustering and extracting useful information from healthcare data. The challenging issue in this domain is to select adequate data mining algorithm for optimal results. This paper exploits three different clustering algorithms: DBSCAN (Density-Based Clustering), agglomerative hierarchical and k-means in real transactional healthcare data of diabetic patients (taken as case study) to analyse their performance in large and dispersed healthcare data. The best solution of cluster sets among the exploited algorithms is evaluated using clustering quality indexes and is selected to identify the possible subgroups of patients having similar treatment patterns |
topic |
Clustering Techniques Data Mining Healthcare Applications Diabetes |
url |
http://publications.muet.edu.pk/research_papers/pdf/pdf845.pdf |
work_keys_str_mv |
AT naeemahmadmahoto exploitationofclusteringtechniquesintransactionalhealthcaredata AT faisalkarimshaikh exploitationofclusteringtechniquesintransactionalhealthcaredata AT abdulqadiransari exploitationofclusteringtechniquesintransactionalhealthcaredata |
_version_ |
1725483998090625024 |