Authors :
Ankita Singha; Anushka Sikdar; Palak Choudhary; Pranati Rakshit; Sonali Bhattacharyya
Volume/Issue :
Volume 7 - 2022, Issue 7 - July
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3oFbGdo
DOI :
https://doi.org/10.5281/zenodo.6946475
Abstract :
Worldwide, machine learning is used in a
variety of fields. Machine learning will be crucial in
determining whether or not heart disorders will exist. If
forecasted long in advance, such information will
provide clinicians with crucial intuitions. The majority
of our work focuses on applying machine learning
algorithms to predict possible heart problems. We tend
to compare classifiers such Naive Bayes, logistical
Regression, SVM, XGBOOST, Random Forest, etc.
during the course of this work. Since it will have a wide
range of samples for coaching and confirmatory
analysis, Random Forest suggests an ensemble classifier
that does hybrid classification by using both strong and
weak classifiers. As a result, we analyse planned and
existing classifiers like Ada-boost and XG-boost that
offer the highest accuracy and prognostication. The best
accuracy is provided by XGBOOST (90.6%).
Keywords :
SVM, Naive Bayes, Random Forest, logistic regression, Ada-boost, XG-boost, Python programming, confusion matrix, and matrix.
Worldwide, machine learning is used in a
variety of fields. Machine learning will be crucial in
determining whether or not heart disorders will exist. If
forecasted long in advance, such information will
provide clinicians with crucial intuitions. The majority
of our work focuses on applying machine learning
algorithms to predict possible heart problems. We tend
to compare classifiers such Naive Bayes, logistical
Regression, SVM, XGBOOST, Random Forest, etc.
during the course of this work. Since it will have a wide
range of samples for coaching and confirmatory
analysis, Random Forest suggests an ensemble classifier
that does hybrid classification by using both strong and
weak classifiers. As a result, we analyse planned and
existing classifiers like Ada-boost and XG-boost that
offer the highest accuracy and prognostication. The best
accuracy is provided by XGBOOST (90.6%).
Keywords :
SVM, Naive Bayes, Random Forest, logistic regression, Ada-boost, XG-boost, Python programming, confusion matrix, and matrix.
