01440nas a2200265   4500000000100000000000100001008004100002260001200043653002000055653001200075653001200087653002400099653000900123653002700132100002900159700003100188700002300219700003600242245008600278856008100364300001000445490000600455520069900461022001401160       2021                            d  c12/202110aBody Mass Index10aC-means10aK-means10aPrehensile Strength10aRisk10aSupport Vector Machine1 aE.F. Bareño-Castellanos1 aPaulo Alonso Gaona-García1 aJ.E. Ortiz-Guzmán1 aCarlos Enrique Montenegro-Marin00aUsing Grip Strength as a Cardiovascular Risk Indicator Based on Hybrid Algorithms  uhttps://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_3_0.pdf  a27-330 v73 aThis 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.  a1989-1660