Predicting heart disease using hybrid machine learning model

Multiple Chronic disease are available especially Heart disease is the foremost reasons of death in modern world. Machine learning (ML) is useful for making conclusions and predictions based on a huge volume of data formed by the healthcare industry. The proposed approach uses machine learning techn...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-05, Vol.1916 (1), p.12208
Main Authors: Renugadevi, G, Asha Priya, G, Dhivyaa Sankari, B, Gowthamani, R
Format: Article
Language:English
Subjects:
Cardiovascular disease
Decision trees
Heart
Heart diseases
Machine learning
Physics
Prediction models
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Summary:Multiple Chronic disease are available especially Heart disease is the foremost reasons of death in modern world. Machine learning (ML) is useful for making conclusions and predictions based on a huge volume of data formed by the healthcare industry. The proposed approach uses machine learning techniques to find heart disease in this study. The prediction model, which employs classification techniques, is based on the Cleveland heart dataset. The Random Forest and Decision Tree machine learning techniques are used. This model for heart ailment with hybrid methodology has an accuracy level of 88.7%, according to experimental study. The boundary is determined as an input parameter from the user to predict heart disease using a Decision Tree method and Random Forest hybrid methodology.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1916/1/012208