Detection of the Onset of Diabetes Mellitus by Bayesian Classifier Based Medical Expert System

  • Md. Mozaharul Mottalib Department of Computer Science and Engineering, Green University, Bangladesh;
  • Md. Mokhlesur Rahman Department of Computer Science and Engineering, Prime University, Bangladesh;
  • Md. Tarek Habib Department of Computer Science and Engineering, Daffodil International University, Bangladesh
  • Farruk Ahmed Department of Computer Science and Engineering, Independent University, Bangladesh
Keywords: Expert system, diagnosis of disease, pattern recognition, classification, Bayesian classifier

Abstract

Expert systems play an important role in medical diagnosis research. Researches are still being conducted for building expert systems capable of diagnosing different diseases. Diabetes mellitus is one of the diseases that have gained attention in the past years. Patients are usually unaware of having this disease and are finally diagnosed with diabetes after several years from onset. Since diabetes can be controlled, it is much desirable to harness it at the onset. Therefore, the prediction of onset of diseases like diabetes has been the point of interest for the researchers. Researchers are continuously trying to formulate an inference engine, a part of an expert system, in order to predict the disease at the beginning. In this paper, we present a Bayesian classification approach to identify the onset of diabetes mellitus in patients using a well-known data set as the sample. We have found an intriguing result with more than 87% accuracy.

Author Biography

Md. Mozaharul Mottalib, Department of Computer Science and Engineering, Green University, Bangladesh;

Lecturer and Program Coordinator (Day),

Department of Computer Science and Engineering

Published
2016-09-14