نظام تشخيص السكري باستخدام خوارزمية ID3 وخوارزمية البيز == Diabetes Diagnosis System Using ID3 and Bayesian Algorithms
Author name:
احلام راشد خرباط
Supervisor name:
زكي سعيد توفيق | سكينة حسن هاشم
General topic:
Computer Science
Specific topic:
Computer Science
Degree:
Master
University:
Mustansiriyah University - College Of Science - Department Of Computer
Language:
English
University location:
Baghdad
First pages:
28T829 - p.pdf
Abstract:
In today’s world, people get affected by many diseases which cannot be completely cured. Diabetes is one of these diseases and is now a big growing health problem. It leads to heart attack, kidney failure and renal disease risks. The techniques of data mining have been widely applied to extract knowledge from medical databases. This work presents a proposed Medical Diagnosis System of Diabetes aiming to identify the correct diagnosis of Patient’s diabetes as quickly as possible and at a lower cost as possible. The Proposal has three subsequent stages; the first stage is to construct the medical dataset (MD) with eight features which are taken for 1000 patients and cover three classes (Diabetic, Non - Diabetic, and Predicted - Diabetic). The second stage is preprocessing the MD by removing redundancy, generalize and normalize some of features values, and to predict the missing values using K - Nearest Neighbor algorithm (KNN) instead of the traditional filling method in which values are estimated based on experiences. The third stage is data mining - based machine learning, which depends on two algorithms Interactive Dichotomizer 3 (ID3) classifier and Traditional Naïve Bayesian (TNB). TNB required an additional checking step to be suitable for the domain; this version has been called Modified Naïve Bayesian (MNB)). The outcome of implementing the proposed system showed that the accuracy of MNB classifier is generally higher than that of both TNB and ID3 classifiers for all feature sets. It has been found that accuracy of the ID3 model is approximately (98.5%), while the accuracy of the TNB model is about (63%) and the accuracy of the MNB model is (100%). The implementation of the proposal has been done using MS. Visual Studio C#.
