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Diabetes Disease Analysis and Prediction by applying Decision Tree, Naïve base and Random Forestalgorithms
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| Author:
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WASEEM NSAIF, LAITH TALIB RASHEED, SADDM HAMDANAHMED, MARAM SAAD NSAIF
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| Abstract:
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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.
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| Keyword:
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Data Mining; Decision tree; Naive base; Random forestClassification; Diabetes
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2019.11.04.069
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| Download:
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