Open AccessPredictive Systems: Role of Feature Selection in Prediction of Heart Disease
Author(s) -
Debjani Panda,
Ratula Ray,
Azian Azamimi Abdullah,
Satya Ranjan Dash
Publication year - 2019
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 - 10.1088/1742-6596/1372/1/012074
Subject(s) - feature selection , random forest , lasso (programming language) , artificial intelligence , machine learning , elastic net regularization , logistic regression , computer science , naive bayes classifier , regression , feature (linguistics) , predictive modelling , logistic model tree , decision tree , selection (genetic algorithm) , pattern recognition (psychology) , support vector machine , statistics , mathematics , linguistics , philosophy , world wide web
As per recent trends heart disease has become the major factor for untimely deaths. There are huge amounts of clinical data available from biomedical devices and various applications used by hospitals. Artificial Intelligence is rigorously being used in predicting conditions of heart patients. This is mainly achieved by machine learning where a model is trained with sample cases and is then used for prediction of the ailment as per data available from clinical tests of the patient. This paper focuses in analyzing the accuracy of various classification algorithms, when they are supervised by set of features. Feature selection plays an important role in eliminating redundant and irrelevant features and reduces the training cost and time of the predictive models. The classification algorithms, which have been analyzed include Naive Bayes, Random Forest, Extra Trees and Logistic regression which have been provided with selected features using least absolute shrinkage and selection operator (LASSO) and Ridge regression. The accuracy of the classifiers shows remarkable improvement after using feature selection. The prediction has improved on an average by 33.3% using Lasso regression as compared to 30.73% using ridge regression.