Using grip strength as a cardiovascular risk indicator based on hybrid algorithms
This article shows the application and design of a hybrid algorithm capable of classifying people into risk groups using data such as prehensile strength, body mass index and percentage of fat. The implementation was done on Python and proposes a tool to help make medical decisions regarding the car...
- Autores:
-
Bareño Castellanos, Edvard Frederick
Gaona Garcia, Paulo Alonso
Ortiz-Guzman, Johan-Enrique
Montenegro Marín, Carlos Enrique
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2021
- Institución:
- Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
- Repositorio:
- Repositorio Institucional UDCA
- Idioma:
- eng
- OAI Identifier:
- oai:repository.udca.edu.co:11158/4522
- Palabra clave:
- Enfermedades cardiovasculares
Indice de masa corporal
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
| Summary: | This article shows the application and design of a hybrid algorithm capable of classifying people into risk groups using data such as prehensile strength, body mass index and percentage of fat. The implementation was done on Python and proposes a tool to help make medical decisions regarding the cardiovascular health of patients. The data were taken in a systematic way, k-means and c-means algorithms were used for the classification of the data, for the prediction of new data two vectorial support machines were used, one for the k-means and the other for the c-means, obtaining as a result a 100% of precision in the vectorial support machine with c-means and a 92% in the one of k-means. |
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