Knowledge Discovery About Cancer Based on Fuzzy Predicates

Miguel Angel Quiroz Martinez, Christian Rene Vargas Alava, Monica Daniela Gomez Rios, Maikel Yelandi Leyva Vazquez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

Knowledge discovery information was analyzed in references regarding the health area. The problem is the lack of a generalized classification of cancer-based on fuzzy predicates, the challenge is the extraction of information from a data set. The objective is to perform a fuzzy predicate based on cancer knowledge discovery analysis to find time optimization of data and results. Empirical - analytical research with a quantitative approach was applied, its method is quasi-experimental and the technique of sampling a specific group of references was used. The application of materials resulted in an Analysis of the relationship between variables, a correlation between Monoplot, and Similarities between observations. It was concluded that the discovery of knowledge generates new quantitative information on the correlations between the attributes belonging to a data set; We use Principal Component Analysis which is a knowledge-based model to analyze positive or negative correlations between attributes or variables.

Idioma originalInglés
Título de la publicación alojadaAdvances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE 2021 Virtual Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy, 2021
EditoresTareq Z. Ahram, Waldemar Karwowski, Jay Kalra
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas242-250
Número de páginas9
ISBN (versión impresa)9783030806231
DOI
EstadoPublicada - 2021
EventoAHFE Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy, 2021 - Virtual, Online
Duración: 25 jul. 202129 jul. 2021

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen271
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

ConferenciaAHFE Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy, 2021
CiudadVirtual, Online
Período25/07/2129/07/21

Nota bibliográfica

Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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