Associative Analysis Among Attribute of ILPD Medical Datasets Using ARM
Ramjeevan Singh Thakur
Ramjeevan Singh Thakur, Department of Computer Applications, MANIT, Bhopal (M.P), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 321-328 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2792028419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Early detection of liver disease plays a major role in efficient diagnosis the disease. It significantly increases the chance of effective treatment. The liver is one of the largest organs in the human body. It plays an important role in digestion, as detoxifying chemicals in the digestion process. A dreadful fact of liver disease is that, the liver maintains a normal functionality even after partially damage. The major challenge in liver disease is to find the hidden patterns of liver disorder. The proposed approach analysis the patterns on the selected features using association rule mining (ARM) technique. The performance of the proposed approach is tested on the well-renowned ILPD dataset from the UCI repository. ILPD dataset consists of different clinical examination parameter like total bilirubin, direct bilirubin, SGPT, SGOT, alkphos, total protein, albumin etc. The proposed approach selected the essential features from ILPD and ARM is applied to find the association among attributes to detect pattern.
Keyword: Indian Liver Patient Datasets, Association Rule Mining, Liver Disorder, Associative Analysis.
Scope of the Article: Predictive Analysis