Cost Effective Approach on Feature Selection Using Genetic Algorithms and Fuzzy logic for Diabetes Diagnosis

Cost effective approach on feature selection using genetic algorithms and fuzzy logic for diabetes diagnosis

A way to enhance the performance of a model that combines genetic algorithms and fuzzy logic for feature selection and classification is proposed. Early diagnosis of any disease with less cost is preferable. Diabetes is one such disease. Diabetes has become the fourth leading cause of death in developed countries and there is substantial evidence that it is reaching epidemic proportions in many...

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Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier

This work focuses on the problem of diagnosing the disease in the earlier stage by applying a selection technique based on genetic algorithm and least square support vector machines. The implementation of the technique analyses the accuracy of the classifier as well as the cost effectiveness in the implementation. This technique will help us to diagnose the disease with a limited number of test...

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Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier

This work focuses on the problem of diagnosing the disease in the earlier stage by applying a selection technique based on genetic algorithm and least square support vector machines. The implementation of the technique analyses the accuracy of the classifier as well as the cost effectiveness in the implementation. This technique will help us to diagnose the disease with a limited number of test...

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Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier

This work focuses on the problem of diagnosing the disease in the earlier stage by applying a selection technique based on genetic algorithm and least square support vector machines. The implementation of the technique analyses the accuracy of the classifier as well as the cost effectiveness in the implementation. This technique will help us to diagnose the disease with a limited number of test...

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Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets

Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...

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Cost Effective Approach on Feature Selection Using Genetic Algorithms and Fuzzy logic for Diabetes Diagnosis

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