Open AccessA Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms
Author(s) -
Pratibha Verma,
Vineet Kumar Awasthi,
Sanat Kumar Sahu
Publication year - 2021
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.350304
Subject(s) - cart , artificial intelligence , support vector machine , computer science , artificial neural network , principal component analysis , ensemble learning , pattern recognition (psychology) , deep learning , machine learning , decision tree , kernel (algebra) , data mining , mathematics , engineering , mechanical engineering , combinatorics
Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.