PREDICTIVE MODEL OF CARDIOVASCULARx DISEASES IMPLEMENTING ARTIFICIAL NEURAL NETWORKS Carlos Henriquez 1 , Johan Mardin 2 , Dixon Salcedo 2 , María Pulgar-Emiliani 3 , Inirida Avendaño 4 , Luis Angulo 2 and Joan Pinedo 2 1 Faculty of Engineering, Universidad del Magdalena, Santa Marta, Colombia 2 Computer Science and Electronics Department, Universidad de la Costa, Barranquilla, Co- lombia 3 Clínica de la Costa, Barranquilla, Colombia 4 Humanities Department, Universidad de la Costa, Barranquilla, Colombia, Colombia chenriquezm@unimagdalena.edu.co Abstract. Currently there is a growing need from health entities in the integra- tion of the use of technology. Cardiovascular disease identification (CEI) sys- tems allow a large extent to predict diseases associated with the heart, thus al- lowing early identification of Cardiovascular Diseases (CVD) to improve the quality of life of patients. In this research, a comparative analysis of the results obtained after implement- ing a series of feature selection techniques (Info.Gain, Gain ratio), and classifi- cation techniques based on artificial neural networks (SOM and GHSOM) was carried out, using the same data set "Heart Cleveland Kaggle Disease Data set" hosted in the Machine Learning UCI repository and under the same test envi- ronment, in order to establish which of the techniques mentioned achieve a higher percentage of accuracy and precision when identifying patients who suf- fer from the disease under study for the performance of the tests, cross- validation was used in order to select a percentage of the data set to perform them and another for training. Through the implementation of load balancing, normalization, and attribute se- lection techniques, it was possible to reduce the number of characteristics used in the classification process of the predictive model of cardiovascular diseases, which generates a reduction in computational requirements, based on the above, 81.45% of successes were obtained with the hybridization of the Gain ratio fea- ture selection technique and the GHSOM training techniques with the use of 7 features. Keywords: SOM Neural Networks, GHSOM Neural Networks, Feature Selec- tion, Cardiovascular disease.