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Optimal design of a fuzzy system with a real-coded genetic algorithm for diabetes classification

Abstract:

In this work a real-coded genetic algorithm for parameter optimization of the membership functions of the inputs and the outputs of a fuzzy inference system applied to diabetes classification is proposed. The main goal of this article is to show the advantages that parameter optimization of all the inputs and output using a real-coded genetic algorithm. The dataset used in this work to validate the approach is the PIMA Indian Diabetes dataset, where we have selected five attributes to perform the parameter optimization. Being the Diabetes a disease that has been affecting many lives in the world, for this reason, this work seeks to find a better classification.

Año de publicación:

2021

Keywords:

    Fuente:

    googlegoogle

    Tipo de documento:

    Other

    Estado:

    Acceso abierto

    Áreas de conocimiento:

    • Aprendizaje automático
    • Algoritmo
    • Diabetes

    Áreas temáticas:

    • Métodos informáticos especiales
    • Fisiología humana

    Contribuidores:

    Daniela Estefanía Sánchez-RodríguezDaniela Estefanía Sánchez-Rodríguez