Green Computing Process and its Optimization Using Machine Learning Algorithm in Healthcare Sector

Handling the information is crucial task in healthcare sector; the data mining techniques will be right choice to address the complex problems. The hybridized optimization techniques in big data analytics consider the important part of the healthcare network communication issues in decision making a...

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Vydáno v:	Mobile networks and applications Ročník 25; číslo 4; s. 1307 - 1318
Hlavní autoři:	Zubar, A. H., Balamurugan, R.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.08.2020 Springer Nature B.V
Témata:	Algorithms Artificial neural networks Cardiovascular disease Clustering Communications Engineering Computation Computer Communication Networks Data mining Decision making Electrical Engineering Engineering Health care Health care industry Heart diseases Heart failure IT in Business Learning theory Machine learning Networks Neural networks Optimization Optimization techniques Risk analysis Support vector machines Heart failure Performance enhancement Comparative analysis Coronary artery disease Spiral KNN classifier Optimization of prediction model
ISSN:	1383-469X, 1572-8153
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Abstract	Handling the information is crucial task in healthcare sector; the data mining techniques will be right choice to address the complex problems. The hybridized optimization techniques in big data analytics consider the important part of the healthcare network communication issues in decision making approach of patient information. This article focused on heart disease data mining and relevant issues since the heart diseases are considered as a reason for causing deaths just as for males and females all over the world. So, people need to be conscious of possible aspects of heart disease. Even though genetics has a part, some of the standards of living practiced are the fundamental reasons for the heart disease. The heart diseases are classified by classical techniques with 13 risk factors and helpful variables. The introduced approach delivers a new computing hybrid modeling scheme for detect the heart diseases. This study represents, various existing methods making decisions for cardio vascular risks depends on the artificial neural networks (ANN). This ANN based methods generally anticipated that Heart Failure attributes having same risk involvement to the heart failure diagnosis. In this article the strategy of an effective recognition method is analyzed for analyzing the failure related to heart diseases using a hybridized approach of K-Nearest Neighbor clustering and Spiral optimization in the classification of the cardio vascular risks. The hybridized KNN technique is matched with some data mining techniques like Support vector Machine (SVM), Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN). The experimental results of this work achieved optimized improved results significantly than other machine learning techniques. The illustrative results exposed that the hybrid scheme stated effectually classify heart disease in the way of computing optimized prediction of heart diseases. Overall the proposed algorithm evidence 5% of enhancement in prediction of heart diseases with comparison of other existing machine learning techniques.
AbstractList	Handling the information is crucial task in healthcare sector; the data mining techniques will be right choice to address the complex problems. The hybridized optimization techniques in big data analytics consider the important part of the healthcare network communication issues in decision making approach of patient information. This article focused on heart disease data mining and relevant issues since the heart diseases are considered as a reason for causing deaths just as for males and females all over the world. So, people need to be conscious of possible aspects of heart disease. Even though genetics has a part, some of the standards of living practiced are the fundamental reasons for the heart disease. The heart diseases are classified by classical techniques with 13 risk factors and helpful variables. The introduced approach delivers a new computing hybrid modeling scheme for detect the heart diseases. This study represents, various existing methods making decisions for cardio vascular risks depends on the artificial neural networks (ANN). This ANN based methods generally anticipated that Heart Failure attributes having same risk involvement to the heart failure diagnosis. In this article the strategy of an effective recognition method is analyzed for analyzing the failure related to heart diseases using a hybridized approach of K-Nearest Neighbor clustering and Spiral optimization in the classification of the cardio vascular risks. The hybridized KNN technique is matched with some data mining techniques like Support vector Machine (SVM), Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN). The experimental results of this work achieved optimized improved results significantly than other machine learning techniques. The illustrative results exposed that the hybrid scheme stated effectually classify heart disease in the way of computing optimized prediction of heart diseases. Overall the proposed algorithm evidence 5% of enhancement in prediction of heart diseases with comparison of other existing machine learning techniques.
Author	Zubar, A. H. Balamurugan, R.
Author_xml	– sequence: 1 givenname: A. H. surname: Zubar fullname: Zubar, A. H. email: aahameed@kau.edu.sa organization: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University – sequence: 2 givenname: R. surname: Balamurugan fullname: Balamurugan, R. organization: Department of Mechanical Engineering, M.Kumarasamy College of Engineering
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Cites_doi	10.1016/j.procs.2017.11.283 10.1007/s10044-015-0452-8 10.1016/j.jacc.2014.12.040 10.1016/j.eswa.2016.10.020 10.1007/s40815-016-0255-0 10.1016/j.knosys.2017.06.026 10.1007/s40012-016-0100-5 10.1109/ACCESS.2017.2694446 10.1007/s00500-016-2080-7 10.1016/j.jocs.2016.01.001 10.1016/j.jbi.2018.03.016 10.1016/j.asoc.2013.09.020 10.1016/j.jacc.2009.12.047 10.1016/j.cmpb.2017.02.001 10.1007/s13534-017-0046-z 10.1016/j.knosys.2015.02.005 10.1016/j.jacc.2015.09.054 10.1016/j.jacc.2014.07.944 10.1016/j.ijepes.2014.04.037 10.1016/j.knosys.2016.02.001 10.1007/s00500-016-2410-9 10.1007/s13246-015-0337-6 10.1016/j.knosys.2015.02.011 10.1016/j.jacc.2013.11.043 10.1016/j.physa.2017.04.113 10.1016/j.cmpb.2013.08.017 10.1016/j.ins.2016.10.013 10.1016/j.asoc.2013.11.009 10.1016/j.patcog.2014.10.032 10.1007/978-81-322-2208-8_46
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SubjectTerms	Algorithms Artificial neural networks Cardiovascular disease Clustering Communications Engineering Computation Computer Communication Networks Data mining Decision making Electrical Engineering Engineering Health care Health care industry Heart diseases Heart failure IT in Business Learning theory Machine learning Networks Neural networks Optimization Optimization techniques Risk analysis Support vector machines
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