Identification of Models-Decision Tree and Random Forest Classifier using Rattle on Diabetes Disease
Aparna Chauhan1, Ankur Garg2
1Aparna Chauhan, Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, Uttarpradesh, India.
2Ankur Garg, Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, Uttarpradesh, India
Manuscript received on 09 August 2019 | Revised Manuscript received on 16 August 2019 | Manuscript Published on 31 August 2019 | PP: 172-176 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I10330789S219/19©BEIESP DOI: 10.35940/ijitee.I1033.0789S219
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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: Diabetes is the disease which is growing now a days in human body and there are a number of patient who are suffering by this diabetes in the world. The data related to medical area is very huge which is related to the many disease. So the first thing is that we have to choose a mining tool which give best result for the given databases. Because, this medical data is statistical and most of the researchers using this type of data. Data mining tool is used for the extracting better result in accuracy for the diabetes data base. By the data mining techniques the medical expert and researchers analyze the result and provide the best treatment for this disease. In this paper we are using diabetes data and apply it on the Rattle, an open source tool of data mining and perform two classification methods decision tree and random forest tree for classify the data and show that which classification algorithm is best for diabetes dataset.
Keywords: Data mining, Diabetes, Rattle tool, Decision Tree, Random Forest Tree
Scope of the Article: Forest Genomics and Informatics