Open AccessMachine Learning Models for Prediction of Cardiovascular Diseases
Author(s) -
K. Sivaraman,
Varun Khanna
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 - 10.1088/1742-6596/2040/1/012051
Subject(s) - feature selection , boosting (machine learning) , support vector machine , lasso (programming language) , artificial intelligence , redundancy (engineering) , gradient boosting , machine learning , computer science , correlation , relevance (law) , pattern recognition (psychology) , selection (genetic algorithm) , mathematics , geometry , random forest , world wide web , political science , law , operating system
Support Vector Machines (SVM) [9], Ada Boost (AB) [10], and Gradient Boosting (GB). Maximum Relevance, Minimum Redundancy (mRMR), Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) are examples of fast correlation-based filters. The authors tested all attributes as well as the selected attributes generated by the above feature selection methods on the Cleveland heart disease dataset (CHDD) and the Hungarian heart disease dataset (HHDD). For their suggested framework, the authors were able to attain the greatest feasible model accuracy of 92.09 percent (all features) and 94.41 percent (selected features). The early detection of cardiac disease was demonstrated by several additional researchers.