Abstract:
Diabetes Mellitus is one of the fatal diseases growing at a rapid rate in developing
countries. This rate is also critical in the developed countries, Diabetes
Mellitus being one of the major contributors to the mortality rate. Detection
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 diagnosis.
As much as there was a need for exact classification with accuracy, it
should be understood that detection of a diabetic situation is highly beneficial
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 improving
the performance by using the optimization methods like Grey Wolf
Optimization (GWO) and Particle Swarm Optimization (PSO).
