Please use this identifier to cite or link to this item:
http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/3034
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DC Field | Value | Language |
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dc.contributor.advisor | Héctor Durán-Muñoz | es_ES |
dc.contributor.other | https://orcid.org/0000-0002-9498-6602 | - |
dc.contributor.other | 0000-0002-9498-6602 | - |
dc.coverage.spatial | Global | es_ES |
dc.creator | Zambrano de la Torre, Misael | - |
dc.creator | Guzmán Fernández, Maximiliano | - |
dc.creator | Sifuentes Gallardo, Claudia | - |
dc.creator | Gamboa Rosales, Hamurabi | - |
dc.creator | Luna García, Huizilopoztli | - |
dc.creator | Sandoval García, Ernesto | - |
dc.creator | Durán Muñoz, Héctor | - |
dc.date.accessioned | 2022-08-29T17:34:29Z | - |
dc.date.available | 2022-08-29T17:34:29Z | - |
dc.date.issued | 2021-08-05 | - |
dc.identifier | info:eu-repo/semantics/publishedVersion | es_ES |
dc.identifier.isbn | 978-967-2948-12-4 | es_ES |
dc.identifier.uri | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/3034 | - |
dc.identifier.uri | http://dx.doi.org/10.48779/ricaxcan-144 | - |
dc.description | Approximately 41 million people in the world die each year from cardiovascular diseases. In Mexico, it is one of the main causes of death per year. This problem is even more critical in rural areas of Mexico. Due to the limited number of specialized medical equipment available in these clinics. Therefore, the objective of this work is to propose a new stage in the methodology used in machine learning for the classification of cardiovascular risk in rural clinics in Mexico. The importance of this work is being able to classify patients based only on non-invasive attributes, avoiding the use of specialized clinical equipment. For this purpose, the Heart Disease Data Set repository is used to implement the new stage. The methodology to be implemented consists of 6 stages. The performance of the three algorithms is compared in terms of four parameters. The results obtained show that only 4 attributes are required for classification with an 80% acceptance rate. | es_ES |
dc.description.abstract | Aproximadamente 41 millones de personas en el mundo mueren cada año por enfermedades cardiovasculares. En México es una de las principales causas de muerte al año. Este problema es aún más crítico en las zonas rurales de México. Debido al número limitado de equipo médico especializado disponible en estas clínicas. Por tanto, el objetivo de este trabajo es proponer una nueva etapa en la metodología utilizada en aprendizaje automático para la clasificación del riesgo cardiovascular en clínicas rurales de México. La importancia de este trabajo es poder clasificar a los pacientes en base únicamente a atributos no invasivos, evitando el uso de equipos clínicos especializados. Para ello, se utiliza el repositorio Heart Disease Data Set para implementar la nueva etapa | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Department of Mathematical Sciences Faculty of Computer & Mathematical Sciences UiTM Kedah | es_ES |
dc.relation | https://36f92a07-7496-48b7-b8c5-d4b3a7a690bd.filesusr.com/ugd/9483e7_fa3419ecd9a748208fc6b7e8d5421225.pdf | es_ES |
dc.relation.ispartof | https://uitmicms.wixsite.com/icms2021/publication | es_ES |
dc.relation.uri | generalPublic | es_ES |
dc.rights | CC0 1.0 Universal | * |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
dc.source | the 5th International Conference on Computing, Mathematics and Statistics (4-5 de Agosto), Malaysia, pp 335-342 | es_ES |
dc.subject.classification | INGENIERIA Y TECNOLOGIA [7] | es_ES |
dc.subject.other | Machine Learning | es_ES |
dc.subject.other | Cardiovascular risk | es_ES |
dc.subject.other | Specialized medical equipment | es_ES |
dc.title | Apply machine learning to predict cardiovascular risk in rural clinics from Mexico | es_ES |
dc.type | info:eu-repo/semantics/bookPart | es_ES |
Appears in Collections: | *Documentos Académicos*-- M. en Ciencias del Proc. de la Info. |
Files in This Item:
File | Description | Size | Format | |
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misael_paper_malasya_final.pdf | Capítulo de libro | 1,68 MB | Adobe PDF | View/Open |
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