Please use this identifier to cite or link to this item: http://10.9.150.37:8080/dspace//handle/atmiyauni/1855
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dc.contributor.authorKavathiya, Hiren R.-
dc.contributor.authorDr. G. C., Bhimani-
dc.date.accessioned2024-11-21T07:26:14Z-
dc.date.available2024-11-21T07:26:14Z-
dc.date.issued2020-
dc.identifier.issn1869-9391-
dc.identifier.urihttp://10.9.150.37:8080/dspace//handle/atmiyauni/1855-
dc.description.abstractIn this paper, we used two models of outlier detection techniques in which the first model talks about Application of Data Mining Techniques for Outlier Mining in Medical Databases and the second model throws light on Outlier Mining in Medical Databases by UsingStatistical Methods. Both the models emphasizes on detecting the outliers in the medical databases by the way of mining through the entire database. First model makes use of the statistical analysis tools for the work and takes care of complicated issues in terms of patient symptoms, diagnoses and behaviors and hence they are said to be the most promising arenas of outlier determination. In second model outliers of 5 datasets; them being leverage, R-standard, R-student, DFFITS, Cook’s D and covariance ratio are taken care off and explained.en_US
dc.language.isoenen_US
dc.publisherGIS Science Journalen_US
dc.relation.ispartofseries7;7-
dc.subjectData Miningen_US
dc.subjectOutliers detectionen_US
dc.subjectStatistical analysisen_US
dc.subjectMedical Databasesen_US
dc.titleNovel method in detecting outliers in medical databaseen_US
dc.typeArticleen_US
Appears in Collections:01. Journal Articles

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