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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 animminent at-tack will be advantageous to a better prevention plan for anupcoming heart attack. Some studies have been conducted for this purpose, butyet the goal has not been reached to prevent a patient from getting such adisease. In this paper, Neural Network trained with Particle Swarm Optimization(PSONN) is used to analyze the input criteria and enhance heart attackanticipation. A real and novel dataset that has been recorded on the disease isused. After preprocessing the data, the features are fed into the system. As aresult, the outcomes from PSONN have been evaluated against those from otheralgorithms. Decision Tree, Random Forest, Neural network trained withBackpropagation (BPNN), and Naive Bayes were among those employed. Then theresults of 100%, 99.2424%, 99.2323%, 81.3131%, and 66.4141% are producedconcerning the mentioned algorithms, which show that PSONN has recorded thehighest accuracy rate among all other tested algorithms.

Heart Attack Classification System using Neural Network Trained with Particle Swarm Optimization