predicting diabetes using genetic algorithm

A Threshold Genetic Algorithm for Diagnosis of Diabetes using Minkowski Distance Method
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Diabetes is rising quickly across the globe. Early detection of diabetes helps to avoid its inception by taking preventive measures. Diagnosis plays vital role in Diabetes treatment otherwise it leads to long-term complications associated costs and risks. In the existing

Genetic algorithm based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset
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Diabetes Mellitus is a dreadful disease characterized by increased levels of glucose in the blood, termed as the condition of hyperglycemia. As this disease is prominent among the tropical countries like India, an intense research is being carried out to deliver a machine

A Hybrid Method of Feature Selection and Neural Network with Genetic Algorithm to Predict Diabetes
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Abstract diabetes is one of the most serious challenges of health care in developing and developed countries. In the medical field, examination of the patient data using different classifications is used to derive a predictive model. A high number of hypotheses is

Local Search-based Enhanced Multi-objective Genetic Algorithm and its Application to the Gestational Diabetes Diagnosis
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In evolutionary computation, several multi-objective genetic algorithms (MOGAs) have been widely used to solve multi-objective optimization problems (MOOPs). The version NSGA-II, developed by is a useful package using a population-based genetic algorithm to

Upgrading the Performance of KNN and Na Bayes in Diabetes Detection with Genetic Algorithm for Feature Selection
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Data mining is a science that is used for finding models and association rules in huge data where other statistical analysis cannot do that. The medical science needs data mining for analyzing massive data and producing predictive models. The purpose of this research is to

A Comparison of Fuzzy Genetic and Neuro Genetic Hybrid Algorithm for the Classification of Diabetes Disease
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Computational Intelligence methods like fuzzy logic, genetic algorithms and artificial neural networks can deal with complex problems which are very difficult to explain in normal classical methods. This paper proposes a comparison of fuzzy and hybrid techniques like

Hybrid algorithm based on K-nearest-neighbor algorithm and Adaboost with selection of feature by genetic algorithms for the diagnosis of diabetes
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Abstract diabetes is known to be one of the chronic and dangerous diseases the people in afflicted with which are increasing in number. On the other hand, early detection of this disease can be effective in treatment and control of disease progression for patients. Using