Heart Disease Prediction using Machine Learning

An important cause fatalities in the modern era is heart disease. Cardiovascular disease detection is a significant challenge in diagnosis data interpretation. The enormous quantities of data that the medical industry produces sector have shown the value of machine learning for generating estimates...

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Bibliographic Details
Main Authors: Raj, Amrit, Kumar, Saurabh, Kumar, Ayush, Budhiraja, Sourabh
Format: Conference Proceeding
Language:English
Subjects:
Decision Tree Classifier
Decision trees
Heart
K-Nearest Neighbor
Logistic regression
Machine Learning
Signal processing
Signal processing algorithms
Static VAr compensators
Support Vector Classifier
Vectors
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Summary:An important cause fatalities in the modern era is heart disease. Cardiovascular disease detection is a significant challenge in diagnosis data interpretation. The enormous quantities of data that the medical industry produces sector have shown the value of machine learning for generating estimates and decisions. In order to assess if a person has heart illness or not utilizing a range of data submitted by patients. The necessity to improve diagnostic accuracy and save human resources in hospitals is what inspired us to conduct this research. To diagnose heart disease in our research, we use a number of methods, including Logistic Regression, KNN, Decision Tree Classifier, Random Forest, and SVC, with SVC having the highest accuracy of 89.6%.
ISSN:2643-444X
DOI:10.1109/ICSC60394.2023.10441292