Application of Apriori Improvement Algorithm in Asthma Case Data Mining

In Chinese medicine, asthma cases contain a large amount of empirical data which are obtained from the clinical diagnosis of doctors throughout the year. Data correlation analysis method is among the common mechanisms which are used to mine association between the (1) prescriptions and prescribers (...

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Autores principales:	Yi Zheng, Peipei Chen, Biyu Chen, Dengjun Wei, Meifang Wang
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/28f7e30f89d04be59d55d35414c7a46c
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spelling	oai:doaj.org-article:28f7e30f89d04be59d55d35414c7a46c2021-11-15T01:19:22ZApplication of Apriori Improvement Algorithm in Asthma Case Data Mining2040-230910.1155/2021/9018408https://doaj.org/article/28f7e30f89d04be59d55d35414c7a46c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9018408https://doaj.org/toc/2040-2309In Chinese medicine, asthma cases contain a large amount of empirical data which are obtained from the clinical diagnosis of doctors throughout the year. Data correlation analysis method is among the common mechanisms which are used to mine association between the (1) prescriptions and prescribers (doctors in this case) and (2) symptoms and medications for a particular disease in the hospitals. In this paper, initially, a thorough analysis of expected performance and shortcomings of the Apriori algorithm in mining of medical case data is presented. Secondly, we propose an extended version of the traditional Apriori algorithm which is primarily based on the fast response of computer to bit-string logic operation. A comparative evaluation of the proposed and existing Apriori algorithms is presented particularly in terms of running time, mining of frequent items set and strong association rules. Both experimental and simulation results have proved that the proposed extended Apriori algorithm has outperformed existing algorithms when it is applied to asthma medication and combined symptom-medication data for the association analysis. Furthermore, the association relationship between mind asthma case data and medication is effective in the analysis of asthma case data with significant application value which is verified by the experimental data and observations.Yi ZhengPeipei ChenBiyu ChenDengjun WeiMeifang WangHindawi 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 Yi Zheng Peipei Chen Biyu Chen Dengjun Wei Meifang Wang Application of Apriori Improvement Algorithm in Asthma Case Data Mining
description	In Chinese medicine, asthma cases contain a large amount of empirical data which are obtained from the clinical diagnosis of doctors throughout the year. Data correlation analysis method is among the common mechanisms which are used to mine association between the (1) prescriptions and prescribers (doctors in this case) and (2) symptoms and medications for a particular disease in the hospitals. In this paper, initially, a thorough analysis of expected performance and shortcomings of the Apriori algorithm in mining of medical case data is presented. Secondly, we propose an extended version of the traditional Apriori algorithm which is primarily based on the fast response of computer to bit-string logic operation. A comparative evaluation of the proposed and existing Apriori algorithms is presented particularly in terms of running time, mining of frequent items set and strong association rules. Both experimental and simulation results have proved that the proposed extended Apriori algorithm has outperformed existing algorithms when it is applied to asthma medication and combined symptom-medication data for the association analysis. Furthermore, the association relationship between mind asthma case data and medication is effective in the analysis of asthma case data with significant application value which is verified by the experimental data and observations.
format	article
author	Yi Zheng Peipei Chen Biyu Chen Dengjun Wei Meifang Wang
author_facet	Yi Zheng Peipei Chen Biyu Chen Dengjun Wei Meifang Wang
author_sort	Yi Zheng
title	Application of Apriori Improvement Algorithm in Asthma Case Data Mining
title_short	Application of Apriori Improvement Algorithm in Asthma Case Data Mining
title_full	Application of Apriori Improvement Algorithm in Asthma Case Data Mining
title_fullStr	Application of Apriori Improvement Algorithm in Asthma Case Data Mining
title_full_unstemmed	Application of Apriori Improvement Algorithm in Asthma Case Data Mining
title_sort	application of apriori improvement algorithm in asthma case data mining
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/28f7e30f89d04be59d55d35414c7a46c
work_keys_str_mv	AT yizheng applicationofaprioriimprovementalgorithminasthmacasedatamining AT peipeichen applicationofaprioriimprovementalgorithminasthmacasedatamining AT biyuchen applicationofaprioriimprovementalgorithminasthmacasedatamining AT dengjunwei applicationofaprioriimprovementalgorithminasthmacasedatamining AT meifangwang applicationofaprioriimprovementalgorithminasthmacasedatamining
_version_	1718428912299540480

Application of Apriori Improvement Algorithm in Asthma Case Data Mining

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