Heart Attack Prediction Based On Machine Learning Techniques

Résumé: The emergence of artificial intelligence and its rapid spread has led to a change in many factors, especially in the medical field, so that it has an effective role in exploring diseases, methods of diagnosis, and assisting in health care in various forms. One of the common diseases that pose a great risk to human health is heart disease, especially heart attacks. Many machine learning models have treated heart diseases in general or heart attacks in particular, but with different models. In this thesis, we wanted to experiment with machine learning techniques, especially classification, to classify a person’s condition as either having the possibility of a heart attack or not having any possibility of it occurring. So we used logistic regression, SVM and ANN to try to reach the highest possible accuracy to provide Greater effectiveness when using these models. To facilitate the use of these models, we have developed a simple website using Flask that enables the user to enter the necessary factors to predict a heart attack.

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