Comparison of The Data-Mining Methods in Predicting The Risk Level of Diabetes

  • Andri Permana Wicaksono Electronic Engineering Polytechnic Institute of Surabaya
  • Tessy Badriyah Electronic Engineering Polytechnic Institute of Surabaya
  • Achmad Basuki Electronic Engineering Polytechnic Institute of Surabaya


Mellitus Diabetes is an illness that happened in consequence of the too high glucose level in blood because the body could not release or use insulin normally. The purpose of this research is to compare the two methods in The data-mining, those are a Regression Logistic method and a Bayesian method, to predict the risk level of diabetes by web-based application and nine attributes of patients data. The data which is used in this research are 1450 patients that are taken from RSD BALUNG JEMBER, by collecting data from 26 September 2014 until 30 April 2015. This research uses performance measuring from two methods by using discrimination score with ROC curve (Receiver Operating Characteristic).  On the experiment result, it showed that two methods, Regression Logistic method and Bayesian method, have different performance excess score and are good at both. From the highest accuracy measurement and ROC using the same dataset, where the excess of Bayesian has the highest accuracy with 0,91 in the score while Regression Logistic method has the highest ROC score with 0.988, meanwhile on Bayesian, the ROC is 0.964. In this research, the plus of using Bayesian is not only can use categorical but also numerical.


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How to Cite
Wicaksono, A. P., Badriyah, T., & Basuki, A. (2016). Comparison of The Data-Mining Methods in Predicting The Risk Level of Diabetes. EMITTER International Journal of Engineering Technology, 4(1), 164-178.