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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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Abstract
Machine learning is utilized in numerous fields worldwide. The medical services industry is no rejection. Predicting the presence or absence of heart diseases, locomotor disorders, and other diseases can all benefit from machine learning. If this information is predicted well in advance, it can give doctors important clues that they can use to adjust their diagnosis and treatment for each patient. Using machine learning algorithms, we try to predict people's risk of developing heart disease. By combining both strong and weak classifiers, our ensemble classifier performs hybrid classification. We analyze both existing classifiers and proposed classifiers like Ada-boost and XG-boost, which can have multiple samples for training and validating the data to provide better accuracy and predictive analysis. This project's comparative analysis focuses on classifiers like decision trees, naive bayes, logistic regression, and random forests.
Keyword
Heart prediction, Decision tree, logistic regression, Naive Bayes, Random Forest, and KNN.
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