Open AccessPredicting heart disease using hybrid machine learning model
Author(s) -
G Renugadevi,
G Asha Priya,
B Dhivyaa Sankari,
R. Gowthamani
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1916/1/012208
Subject(s) - machine learning , decision tree , random forest , heart disease , computer science , artificial intelligence , tree (set theory) , boundary (topology) , medicine , mathematics , cardiology , mathematical analysis
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.