Heart attack prediction using machine learning

Mohamed Wed Eladham; Ali Bou Nassif; Mohammad AlShabi

doi:10.1117/12.2664047

14 June 2023 Heart attack prediction using machine learning

Mohamed Wed Eladham, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12548, Smart Biomedical and Physiological Sensor Technology XX; 125480F (2023) https://doi.org/10.1117/12.2664047
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

The most prevalent kind of cardiovascular illness is a heart attack, which may or may not have symptoms. The damage to the heart muscle increases with delayed treatment, which increases the risk of mortality. More than 10 million people die each year from heart attacks, and many of them may be avoided if heart attacks could be accurately predicted. To estimate the likelihood of suffering a heart attack, five different machine learning algorithms are used on the Public Health Heart Attack dataset. Several evaluation metrics, including accuracy, recall, precision, ROC curve, and F-score, were used to evaluate the models. All the models—MLP, RBF, SVM, KNN, and RF— achieved significant accuracies of more than 75%, with KNN having the greatest overall performance

Citation Download Citation

Mohamed Wed Eladham, Ali Bou Nassif, and Mohammad AlShabi "Heart attack prediction using machine learning", Proc. SPIE 12548, Smart Biomedical and Physiological Sensor Technology XX, 125480F (14 June 2023); https://doi.org/10.1117/12.2664047

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available