Effective Heart Attack Prediction method using Machine Learning Algorithm

In today's era development cardiovascular disease is the most common disease that cause a heart attack. So, diagnosing heart disease patients on time is the most challenging task in the medical field. Due to expensive treatment the poor people prohibit to take over heart disease treatment. The...

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Bibliographic Details
Published in2024 10th International Conference on Communication and Signal Processing (ICCSP) pp. 1723 - 1727
Main Authors Chitra, M.G., Govindaraj, Ramya
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.04.2024
Subjects
Cardiac arrest
Healthcare
Heart
KNN
Machine Learning
Machine learning algorithms
Naïve Bayes
Prediction algorithms
Predictive models
RF and XGBoost
Signal processing algorithms
Support vector machines
SVM
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Summary:In today's era development cardiovascular disease is the most common disease that cause a heart attack. So, diagnosing heart disease patients on time is the most challenging task in the medical field. Due to expensive treatment the poor people prohibit to take over heart disease treatment. The cardiologist's doctors generally predict the patient's heart attack with the help of a scrutinized patient report, electrocardiography, blood tests, computer report based on the patient's previous database before seriousness of patient diseases. This study aims to analyze different machine learning (ML) algorithms that are currently available for predicting heart attacks, including decision trees, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression. Finally, we obtained the best algorithm from this existing work based on accuracy, precision and recall.
ISSN:2836-1873
DOI:10.1109/ICCSP60870.2024.10544225