A new ensemble heart attack diagnosis (EHAD) model using artificial intelligence techniques

Myocardial infarctions, also known as heart attacks, are a leading cause of death globally, highlighting the need for prompt and precise diagnoses to improve patient outcomes. Recently, many machine learning (ML) and artificial intelligence (AI) techniques have been used for diagnosing heart attack diseases, but these techniques still cannot provide the most accurate results. Thus, it is important to improve these approaches to provide better results than current methods do. In this paper, a new hybrid diagnostic approach for heart attack diagnosis called the ensemble heart attack diagnosis model (EHAD), which is based on the ensemble classification technique (ECT), is introduced. ECT integrates three primary classifiers, namely, the support vector machine (SVM), long short-term memory (LSTM), and artificial neural network (ANN), which are combined with the majority voting (MV) technique to make accurate and fast final decisions. The simulation proved that the Ensemble Heart Attack Diagnosis (EHAD) outperforms other models related to many metrics, such as recall, precision, F1 score, accuracy, and many other statistical analysis measurements.