Heart Attack Prediction With Svm

Hanna Willa Dhany

Abstract


Heart attacks are one of the leading causes of death worldwide, making them an important focus in medical prevention and treatment efforts. Early detection of heart attack risk is crucial to provide timely medical intervention and prevent further complications. In this study, we used Support Vector Machine (SVM), one of the effective machine learning algorithms, to predict heart attacks. SVM works by finding the optimal hyperplane that separates data into different classes. We analyzed various risk factors such as age, gender, blood pressure, cholesterol levels, and medical history to predict the likelihood of a heart attack. The results showed that the developed SVM model had an accuracy rate of 91.80%, indicating that SVM can be a reliable prediction tool. This model is expected to help medical personnel make better decisions and provide more personalized care to high-risk patients, as well as contribute significantly to the treatment and prevention of heart attacks.

Keywords


Heart attacks, Support Vector Machine, age, gender, blood pressure, cholesterol levels, and medical history.

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