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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

Predictive Accuracy For Two Diabetes Datasets Using Ant-Miner Algorithm

[Full Text]



Nur Hadirah Khairul Anwar, Rizauddin Saian



AdaBoostM1, Ant-Miner, Data mining, Diabetes, K-nearest neighbors, Naïve Bayes, RIPPER



Data mining is helpful in turning a large amount of data into useful knowledge, therefore, it is beneficial in the medical field. The knowledge that being extracted can assist medical experts in enhancing the diagnosis and treatment of disease. Diabetes mellitus (DM) is a serious health challenge in most developed countries. It is a condition in which the body produces an insufficient amount of insulin to regulate the amount of sugar in the blood. Two diabetes datasets used in this study is Pima Indian diabetes dataset and Frankfurt Germany diabetes dataset. The aim of this paper is to improve predictive accuracy for diabetes by implementing Ant-Miner algorithm to the diabetes datasets and the results obtained will be compared with the result derived from using the different machine learning model such as Naïve Bayes, AdaBoostM1, K-nearest neighbors and RIPPER.



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