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INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH

A Step Towards Excellence
Published by : Advanced Scientific Research
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0975-2366
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IJPR 9[3] July - September 2017 Special Issue

July - September 9[3] 2017

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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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