An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction

In recent years, big data has become an important branch of computer science. However, without AI, it is difficult to dive into the context of data as a prediction term, relying on a large feature of improving the process of prediction is connected with big data modelling, which appears to be a sign...

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Autores principales:	Ahmad Althunibat, Wael Alzyadat, Mohammad Muhairat, Aysh Alhroob, Ikhlas H. Almukahel
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/0953d71c06404fb9b7aed1ce115176eb
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spelling	oai:doaj.org-article:0953d71c06404fb9b7aed1ce115176eb2021-11-15T01:20:15ZAn Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction2040-230910.1155/2021/3870147https://doaj.org/article/0953d71c06404fb9b7aed1ce115176eb2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3870147https://doaj.org/toc/2040-2309In recent years, big data has become an important branch of computer science. However, without AI, it is difficult to dive into the context of data as a prediction term, relying on a large feature of improving the process of prediction is connected with big data modelling, which appears to be a significant aspect of improving the process of prediction. Accordingly, one of the basic constructions of the big data model is the rule-based method. Rule-based method is used to discover and utilize a set of association rules that collectively represent the relationships identified by the system. This work focused on the use of the Apriori algorithm for the investigations of constraints from panel data using the discretization preprocess technique. The statistical outcomes are associated with the improved preprocess that can be applied over the transaction and it can illustrate interesting rules with confidence approximately equal to one. The minimum support provided to the present rule considers constraint as a milestone for the prediction model. The model makes an effective and accurate decision. In nowadays business, several guidelines have been produced. Moreover, the generation method was upgraded because of an association data algorithm that works for dissimilar principles of the structures compared with fewer breaks that are delivered by the discretization technique.Ahmad AlthunibatWael AlzyadatMohammad MuhairatAysh AlhroobIkhlas H. AlmukahelHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Ahmad Althunibat Wael Alzyadat Mohammad Muhairat Aysh Alhroob Ikhlas H. Almukahel An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction
description	In recent years, big data has become an important branch of computer science. However, without AI, it is difficult to dive into the context of data as a prediction term, relying on a large feature of improving the process of prediction is connected with big data modelling, which appears to be a significant aspect of improving the process of prediction. Accordingly, one of the basic constructions of the big data model is the rule-based method. Rule-based method is used to discover and utilize a set of association rules that collectively represent the relationships identified by the system. This work focused on the use of the Apriori algorithm for the investigations of constraints from panel data using the discretization preprocess technique. The statistical outcomes are associated with the improved preprocess that can be applied over the transaction and it can illustrate interesting rules with confidence approximately equal to one. The minimum support provided to the present rule considers constraint as a milestone for the prediction model. The model makes an effective and accurate decision. In nowadays business, several guidelines have been produced. Moreover, the generation method was upgraded because of an association data algorithm that works for dissimilar principles of the structures compared with fewer breaks that are delivered by the discretization technique.
format	article
author	Ahmad Althunibat Wael Alzyadat Mohammad Muhairat Aysh Alhroob Ikhlas H. Almukahel
author_facet	Ahmad Althunibat Wael Alzyadat Mohammad Muhairat Aysh Alhroob Ikhlas H. Almukahel
author_sort	Ahmad Althunibat
title	An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction
title_short	An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction
title_full	An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction
title_fullStr	An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction
title_full_unstemmed	An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction
title_sort	approach to acquire the constraints using panel big data hybrid association rule and discretization process for breast cancer prediction
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/0953d71c06404fb9b7aed1ce115176eb
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An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction

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