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IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Improve Class Prediction By Balancing Class Distribution For Diabetes Dataset

[Full Text]



Mohammad Al Khaldy, Mohammad Alauthman, Majed S. Al-Sanea and Ghassan Samara



Imbalance class; Resampling; Random Forest, Naive Bayes, Bagging.



When using machine-learning algorithms to analyses clinical data, some challenges are facing this kind of data. One of the limitations of data is class imbalance because class imbalance could create a suboptimal performance of the classifier. The purpose of this article is to evaluate the influence of imbalance class on classification efficiency for multiple classification methods. In addition, we resample data by random replacement technique with replacement and without replacement to see how balancing data can improve the performance of classification techniques. The experiments show that resampling with imbalanced replacement class obtains a considerable boost in classification effectiveness for most of the learning algorithms used, but after resampling class, the Naive Bayes algorithm has not been improved.



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