Hybrid Heart Disease Prediction Model using Machine Learning Algorithm


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.

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