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INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH

A Step Towards Excellence

IJPR included in UGC-Approved List of Journals - Ref. No. is SL. No. 4812 & J. No. 63703

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0975-2366
5 - Years Impact Factor

Year 2012 - 2016

Impact Factor: 1.55

Total Publications: 317

Total Citation: 491

Year 2011 - 2015

Impact Factor: 1.46

Total Publications: 326

Total Citation: 477

Year 2010 - 2014

Impact Factor: 1.3

Total Publications: 313

Total Citation: 407

Year 2009 - 2013

Impact Factor: 0.973

Total Publications: 293

Total Citation: 285

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Diabetes Disease Analysis and Prediction by applying Decision Tree, Naïve base and Random Forestalgorithms

Author: WASEEM NSAIF, LAITH TALIB RASHEED, SADDM HAMDANAHMED, MARAM SAAD NSAIF
Abstract: Healthcare data mainly contains all the patients’ information as well as the parties involved in healthcare industries. The rate storage of such type of data is increased very rapidly. Because of the continuous increasing the size of electronic healthcare data becomes very complex. It becomes very difficult to extract the meaningful information from it by using the traditional methods. Due to advancement in field of statistics, mathematics and every other discipline, now it is possible to extract the meaningful patterns from it and can be used for medical decision making. Data mining is beneficial in such a situation where large collections of healthcare data are available. This paper includes a comparison between Decision tree, Naïve baseand Random Forest algorithms on Diabetes dataset. After data classification, the results show that Random Forest algorithm had the more accurate classification with 98% accuracy.
Keyword: Data Mining; Decision tree; Naive base; Random forestClassification; Diabetes
DOI: https://doi.org/10.31838/ijpr/2019.11.04.069
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