Heart Disease Prediction Propagation approach

International Journal of Machine Learning and Networked Collaborative Engineering

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Field Value
Title Heart Disease Prediction Propagation approach
Creator Nagaprasad, Sriramula
T, Pushpalatha Reddy
S, Naga Lakshmi
Subject Support Vector Machine, Backpropagation, Heart disease forecast
Description Data mining methods are used to test complicated data and regression processing on the basis of input data sets is used for the estimation of results. A variety of prediction analysis methods have been implemented in recent years. The clustering method k-means and SVM ( support vector machine) are a statistical computational technique for clustering and defining main data for the detection of cardiac disorders in this study. The Back Propagation Method is used in tandem with k-means clustering algorithm to cluster knowledge for improved prediction research performance. The output of the implemented algorithm is found in the cardiac disorder data sample collected from the UCI depositor. Within this sample, there are 66 attributes. Nonetheless, a subgroup of 14 qualities is needed for every study. The Cleveland platform is utilized in particular for machine-learning investigators. The research designed correlates with the current techniques, precision, error identification and deployment time (using the numerical mean).
Publisher SR Informatics, New Delhi, India
Date 2020-10-24
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Identifier http://www.mlnce.net/index.php/Home/article/view/139
Source International Journal of Machine Learning and Networked Collaborative Engineering; Vol. 4 No. 2 (2020): Volume No 04 Issue No 02 (2020); 72-77
Language eng
Relation http://www.mlnce.net/index.php/Home/article/view/139/82
Rights Copyright (c) 2020 International Journal of Machine Learning and Networked Collaborative Engineering

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