Introduction The cardiovascular disease (CVD) is the main world cause of morbidity and mortality, continually, are searching early diagnostic strategies with enough sensibility and specificity to be used massively in the population. An artificial neural network (ANN) uses mathematical algorithms to predict an event, the vantage of an ANN is the capacity of learning. Objective To develop an ANN, to prediction cardiovascular risk using anthropometric variables, age, and habits. Methods Was analyzed 256 subjects, aged 16 to 60 years old, the ANN was feed with the variables: age, gender, smoking, fat percentage, visceral fat and muscular percentage, the estimation of body composition was made using bioimpedance, the ANN training was made with 183 subjects (69%), the output variable was the CVD probability to ten years prognosticated using ASSIGN scale, the classification was high risk > 10%, moderate risk 5 – 10%, low risk 1 – 4,9%, very low risk < 1%. The ANN was a multi‐layer perceptron trained with the rest blood pressure, total cholesterol, HDL Cholesterol, LDL Cholesterol, and Triglycerides data; and the familiar antecedents of diabetes, cardiovascular disease, and cerebrovascular disease, the software used was Matlab. Results The model show significant differences in the blood chemical variables and the body composition between groups p < 0.0001. The area under the curve for the prediction was: high risk = 0.999, moderate risk = 0.967, low risk = 0.986, very low risk = 0.981. The sensitivity was between 0.750 y 1.000 and specificity was between 0.833 and 1.000. Conclusion An ANN that uses like prediction variable the body composition has a high predictive value, besides it is a good tool for the cardiovascular risk estimation and can be used for the screening in different populations. The project wants to evaluate the population during the following 5 years to determinate the real prediction power of the ANN. Support or Funding Information The project was funding by the unit of research of the Universidad Santo Tomás, with the support of the department of humanities. Diagram of the network developed to evaluate cardiovascular risk. image Diagram of the network developed to evaluate cardiovascular risk. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .