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.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

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.Incluye referencias bibliográficas