Classification of Diabetic Patients using Computational Intelligent Techniques

Abstract

Diabetes Mellitus is one of the fatal diseases growing at a rapid rate in de-

veloping countries. This rate is also critical in the developed countries, Dia-

betes Mellitus being one of the major contributors to the mortality rate. De-

tection and diagnosis of Diabetes at an early stage is the need of the day. It

is required that a classifier is be designed so as to work efficient, convenient

and most importantly, accurate.

Artificial Intelligence and Soft Computing Techniques mimic a great deal of

human ideologies and are encouraged to involve in human related fields of

application. These systems most fittingly find a place in the medical diag-

nosis. As much as there was a need for exact classification with accuracy, it

should be understood that detection of a diabetic situation is highly benefi-

cial to the community. The propose number of research methods expected

for detection of the diabetic conditions so as to provide a sound warning

before they had happened.

The experimental result done using Pima Indian dataset which can even be

retrieved from UCI Machine Learning Repositorys web site.

In this research Genetic Programming Toolbox For Multigene Symbolic

Regression (GPTIPS), used to build a mathematical model for predict the

diabetes class. After that simplified the model by selecting the weighted

features that affected on the prediction model. The Neural Network, Fuzzy

logic and Genetic Programming are used to check the accuracy when using

the new features.

The conclusion of that three features can be used to predict the class. The

mathematical model become simple and convenient. As a feature work im-

proving the performance by using the optimization methods like Grey Wolf

Optimization (GWO) and Particle Swarm Optimization (PSO).