Heart disease is one of the foremost health problems nowadays, and deadliest human disease around the world. It is the main reason for the enormous range of deaths in the world over the previous few decades. Therefore, there is a need to diagnose it in an exceeding specific time to avoid abandoned dangers. In this paper, we propose a hybrid approach to heart disease prediction by using a given range of feature vectors. Furthermore, a comparison of several classifiers for the prediction of heart disease cases with a minimum number of feature vectors are carried out. We proposed two different optimization algorithms like genetic algorithm (GA), and particle swarm optimization (PSO) for feature selection, and convolution neural network (CNN) for classification. The hybrid of GA and CNN is known as genetic neural network (GCNN), and hybrid of PSO and CNN now as particle neural network (PCNN). The experimental results show that accuracy values obtained by PCNN is approximately 82% and GCNN is 75.51%.