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 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.

• Autores: Bareño Castellanos, Edvard Frederick; Montenegro Marin, Carlos Enrique
• Tipo de recurso: Trabajo de grado de pregrado
• Fecha de publicación: 2021
• Institución: Universidad Distrital Francisco José de Caldas
• Repositorio: RIUD: repositorio U. Distrital
• Idioma: spa
• OAI Identifier: oai:repository.udistrital.edu.co:11349/28228
• Acceso en línea: http://hdl.handle.net/11349/28228
• Palabra clave: Body Mass Index; C-Means; K-Means; Percentage f Fat; Prehensile Strength; Risk Indicator; Support Vector Machine; Ingeniería de Sistemas - Tesis y disertaciones académicas; Enfermedades cardiovasculares - Prevención; Algoritmos híbridos; Máquinas de soporte vectorial; Análisis de datos; Body Mass Index; C-Means; K-Means; Percentage f Fat; Prehensile Strength; Risk Indicator; Support Vector Machine.
• License: Atribución-NoComercial-SinDerivadas 4.0 Internacional