Machine Learning Techniques in Predicting Heart Disease a Survey

Roy, Riya Elizabeth; Kulkarni, Praveen; Kumar, Sandeep

doi:10.1109/aic55036.2022.9848945

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) 2022

DOI: 10.1109/aic55036.2022.9848945

|View full text |Cite

Machine Learning Techniques in Predicting Heart Disease a Survey

Riya Elizabeth Roy

Praveen Kulkarni

Sandeep Kumar

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Riya Elizabeth Roy, Praveen Kulkarni, Sandeep Kumar [4] The paper covers various aspects of the problem, including feature selection, preprocessing, and model selection. The authors begin by introducing the problem of heart disease prediction and its importance in healthcare.…”

mentioning

confidence: 99%

Analysis of Cardiovascular Disease using Machine Learning Techniques

Dabir,

Khedkar,

Panch

et al. 2023

IJRASET

View full text Add to dashboard Cite

Detecting cardiovascular disease early is crucial in healthcare, especially in the field of cardiology. With 12 million deaths worldwide annually, the disease is a significant health concern. Early detection can lead to lifestyle changes that reduce the risk of complications. Machine learning techniques can be used to extract useful data from large datasets and make accurate predictions, requiring less investment. Machine learning algorithms can also be used to address unbalanced datasets and feature selection, which can improve diagnostic accuracy. Using ensemble learning with a machine learning algorithm, feature selection, and biomedical test values can help classify cardiovascular disease. Multiple classifier models can also be applied to improve accuracy with an ensemble classifier.

show abstract

mentioning

confidence: 99%

Analysis of Cardiovascular Disease using Machine Learning Techniques

Dabir,

Khedkar,

Panch

et al. 2023

IJRASET

View full text Add to dashboard Cite

show abstract

Learning-based techniques for heart disease prediction: a survey of models and performance metrics

Bizimana,

Zhang,

Asim

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Machine Learning Techniques in Predicting Heart Disease a Survey

Cited by 2 publications

References 0 publications

Analysis of Cardiovascular Disease using Machine Learning Techniques

Analysis of Cardiovascular Disease using Machine Learning Techniques

Learning-based techniques for heart disease prediction: a survey of models and performance metrics

Contact Info

Product

Resources

About