Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility

To enhance the depth of excavation and promote the intelligence of acupoint compatibility, a method of constructing weighted network, which combines the attributes of acupoints and supervised learning, is proposed for link prediction. Medical cases of cervical spondylosis with acupuncture treatment...

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Autores principales:	Xia Qiu, Xiaoying Zhong, Honglai Zhang
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/893beeda4eda45c983b4a86f547410c3
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spelling	oai:doaj.org-article:893beeda4eda45c983b4a86f547410c32021-11-08T02:37:06ZApplied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility2040-230910.1155/2021/4699420https://doaj.org/article/893beeda4eda45c983b4a86f547410c32021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4699420https://doaj.org/toc/2040-2309To enhance the depth of excavation and promote the intelligence of acupoint compatibility, a method of constructing weighted network, which combines the attributes of acupoints and supervised learning, is proposed for link prediction. Medical cases of cervical spondylosis with acupuncture treatment are standardized, and a weighted network is constructed according to acupoint attributes. Multiple similarity features are extracted from the network and input into a supervised learning model for prediction. And, the performance of the algorithm is evaluated through evaluation indicators. The experiment finally screened 67 eligible medical cases, and the network model involved 141 acupoint nodes with 1048 edge. Except for the Preferential Attachment similarity index and the Decision Tree model, all other similarity indexes performed well in the model, among which the combination of PI index and Multilayer Perception model had the best prediction effect with an AUC value of 0.9351, confirming the feasibility of weighted networks combined with supervised learning for link prediction, also as a strong support for clinical point selection.Xia QiuXiaoying ZhongHonglai ZhangHindawi 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 Xia Qiu Xiaoying Zhong Honglai Zhang Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility
description	To enhance the depth of excavation and promote the intelligence of acupoint compatibility, a method of constructing weighted network, which combines the attributes of acupoints and supervised learning, is proposed for link prediction. Medical cases of cervical spondylosis with acupuncture treatment are standardized, and a weighted network is constructed according to acupoint attributes. Multiple similarity features are extracted from the network and input into a supervised learning model for prediction. And, the performance of the algorithm is evaluated through evaluation indicators. The experiment finally screened 67 eligible medical cases, and the network model involved 141 acupoint nodes with 1048 edge. Except for the Preferential Attachment similarity index and the Decision Tree model, all other similarity indexes performed well in the model, among which the combination of PI index and Multilayer Perception model had the best prediction effect with an AUC value of 0.9351, confirming the feasibility of weighted networks combined with supervised learning for link prediction, also as a strong support for clinical point selection.
format	article
author	Xia Qiu Xiaoying Zhong Honglai Zhang
author_facet	Xia Qiu Xiaoying Zhong Honglai Zhang
author_sort	Xia Qiu
title	Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility
title_short	Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility
title_full	Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility
title_fullStr	Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility
title_full_unstemmed	Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility
title_sort	applied research on the combination of weighted network and supervised learning in acupoints compatibility
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/893beeda4eda45c983b4a86f547410c3
work_keys_str_mv	AT xiaqiu appliedresearchonthecombinationofweightednetworkandsupervisedlearninginacupointscompatibility AT xiaoyingzhong appliedresearchonthecombinationofweightednetworkandsupervisedlearninginacupointscompatibility AT honglaizhang appliedresearchonthecombinationofweightednetworkandsupervisedlearninginacupointscompatibility
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Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility

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