  The prior detection of a heart attack could lead to the saving of one's life.
Putting specific criteria into a system that provides an early warning of an
imminent at-tack will be advantageous to a better prevention plan for an
upcoming heart attack. Some studies have been conducted for this purpose, but
yet the goal has not been reached to prevent a patient from getting such a
disease. In this paper, Neural Network trained with Particle Swarm Optimization
(PSONN) is used to analyze the input criteria and enhance heart attack
anticipation. A real and novel dataset that has been recorded on the disease is
used. After preprocessing the data, the features are fed into the system. As a
result, the outcomes from PSONN have been evaluated against those from other
algorithms. Decision Tree, Random Forest, Neural network trained with
Backpropagation (BPNN), and Naive Bayes were among those employed. Then the
results of 100%, 99.2424%, 99.2323%, 81.3131%, and 66.4141% are produced
concerning the mentioned algorithms, which show that PSONN has recorded the
highest accuracy rate among all other tested algorithms.
