Diabetes diagnosis system using modified Naive Bayes classifier

Jwan Kanaan Alwan, Dhulfiqar Saad Jaafar, Itimad Raheem Ali

Abstract


In today’s world, Diabetes is one of these diseases and is now a big growing health problem. The techniques of data mining have been widely applied to extract knowledge from medical databases. In this work, a Medical Diagnosis system of Diabetes is proposed for the ‎diagnosis of diabetes in a manner ‎that is rapid and cost-effective. three stages are ‎involved in the proposed diabetes diagnosis system (DDS) including: dataset constructing, preprocessing and classification algorithm using traditional Naïve Bayesian ‎‎(TNB) and modified Naïve Bayesian (MNB)). MNB Classifier is a modified NB that is used to ‎enhance the accuracy of ‎diagnosis, by adding a proposed modest model to help separate ‎the overlapping diagnosis classes. The outcome‎ ‎showed that the accuracy of MNB classifier is generally higher than that of ‎TNB ‎classifier for all sets of features. An accuracy of about (63%) was achieved for the TNB ‎model, whereas ‎that of the MNB model is (100%). The experimental results showed that ‎the MNB is better than the traditional ‎NB in both two cases of constructed medical ‎datasets; the first case of filling the missing values by experiences and ‎the second case of filling ‎missing values by K-nearest neighbor (KNN) algorithm.

Keywords


Data Mining; Diabetes diagnosis system; K-nearest neighbor algorithm; Modified Naïve Bayesian; Traditional Naïve ‎Bayesian

Full Text:

PDF


DOI: http://doi.org/10.11591/ijeecs.v28.i3.pp1766-1774

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

shopify stats IJEECS visitor statistics