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Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data

Shang-ming Zhou Orcid Logo, Ronan Lyons Orcid Logo, Sinead Brophy Orcid Logo, Mike B Gravenor, Michael Gravenor Orcid Logo

PLoS ONE, Volume: 7, Issue: 12, Start page: e51468

Swansea University Authors: Shang-ming Zhou Orcid Logo, Ronan Lyons Orcid Logo, Sinead Brophy Orcid Logo, Michael Gravenor Orcid Logo

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DOI (Published version): 10.1371/journal.pone.0051468

Abstract

In the identification of non-linear interactions between variables, the Takagi-Sugeno (TS) fuzzy rule system as a widely used data mining technique suffers from the limitations that the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). H...

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Published in: PLoS ONE
ISSN: 1932-6203
Published: 2012
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URI: https://cronfa.swan.ac.uk/Record/cronfa13931
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However, few robust methods are available to tackle this issue, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. In this study, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and &#x3C9;-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. 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spelling 2019-07-17T12:01:31.4545222 v2 13931 2013-01-21 Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 84f5661b35a729f55047f9e793d8798b 0000-0001-7417-2858 Sinead Brophy Sinead Brophy true false 70a544476ce62ba78502ce463c2500d6 0000-0003-0710-0947 Michael Gravenor Michael Gravenor true false 2013-01-21 MEDS In the identification of non-linear interactions between variables, the Takagi-Sugeno (TS) fuzzy rule system as a widely used data mining technique suffers from the limitations that the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). However, few robust methods are available to tackle this issue, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. In this study, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data. Journal Article PLoS ONE 7 12 e51468 1932-6203 Health informatics, data mining, interactions, epidemiology, rule modelling, deprivation 14 12 2012 2012-12-14 10.1371/journal.pone.0051468 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University 2019-07-17T12:01:31.4545222 2013-01-21T09:55:27.1766345 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Shang-ming Zhou 0000-0002-0719-9353 1 Ronan Lyons 0000-0001-5225-000X 2 Sinead Brophy 0000-0001-7417-2858 3 Mike B Gravenor 4 Michael Gravenor 0000-0003-0710-0947 5 0013931-26042019162247.pdf journal.pone.0051468.pdf 2019-04-26T16:22:47.0900000 Output 854417 application/pdf Version of Record true 2019-04-26T00:00:00.0000000 Distributed under the terms of a Creative Commons Attribution (CC-BY-4.0) true eng
title Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data
spellingShingle Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data
Shang-ming Zhou
Ronan Lyons
Sinead Brophy
Michael Gravenor
title_short Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data
title_full Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data
title_fullStr Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data
title_full_unstemmed Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data
title_sort Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data
author_id_str_mv 118578a62021ba8ef61398da0a8750da
83efcf2a9dfcf8b55586999d3d152ac6
84f5661b35a729f55047f9e793d8798b
70a544476ce62ba78502ce463c2500d6
author_id_fullname_str_mv 118578a62021ba8ef61398da0a8750da_***_Shang-ming Zhou
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
84f5661b35a729f55047f9e793d8798b_***_Sinead Brophy
70a544476ce62ba78502ce463c2500d6_***_Michael Gravenor
author Shang-ming Zhou
Ronan Lyons
Sinead Brophy
Michael Gravenor
author2 Shang-ming Zhou
Ronan Lyons
Sinead Brophy
Mike B Gravenor
Michael Gravenor
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container_title PLoS ONE
container_volume 7
container_issue 12
container_start_page e51468
publishDate 2012
institution Swansea University
issn 1932-6203
doi_str_mv 10.1371/journal.pone.0051468
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
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description In the identification of non-linear interactions between variables, the Takagi-Sugeno (TS) fuzzy rule system as a widely used data mining technique suffers from the limitations that the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). However, few robust methods are available to tackle this issue, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. In this study, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data.
published_date 2012-12-14T00:30:08Z
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