Classification of Diabetic Patients using Computational Intelligent Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.8204

@PhdThesis{Elhussein:thesis,

• author = "Ahlam Ali Sharif {Elhussein}",
• title = "Classification of Diabetic Patients using Computational Intelligent Techniques",
• school = "Sudan University of Science and Technology",
• year = "2018",
• address = "Sudan",
• month = mar,
• keywords = "genetic algorithms, genetic programming, UCI Pima Indian, ANN, MLP, KNN, SVM, Multigene Symbolic Regression GP, GPTIPS, Matlab, Fuzzy, PSO",
• URL = "http://repository.sustech.edu/handle/123456789/20889",
• URL = "http://repository.sustech.edu/bitstream/handle/123456789/20889/Classi%ef%ac%81cation%20of%20Diabetic......pdf",
• size = "127 pages",
• 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 Repository's 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).",

• notes = "Supervisor: Mohamed Elhafiz

  Co-Supervisor: Talat Wahabi",

}

Genetic Programming entries for Ahlam Ali Sharif Elhussein

Citations