Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/32472 https://doi.org/Rojo, J., Pinho, L.G., Fonseca, C., Lopes, M.J., Helal, A., Hernández, J., Murillo, J., Garcia-Alonso, J. (2022). Analyzing the Performance of Feature Selection on Regression Problems: a Case Study on Older Adults' Functional Profile. IEEE Transactions on Emerging Topics in Computing. https://doi.org/10.1109/TETC.2022.3181679 https://doi.org/10.1109/TETC.2022.3181679 |
Resumo: | Healthcare systems are capable of collecting a significant number of patient health-related parameters. Analyzing them to find the reasons that cause a given disease is challenging. Feature Selection techniques have been used to address this issue—reducing these parameters to a smaller set with the most ”determinant” information. However, existing proposals usually focus on classification problems—aimed to detect whether a person is or is not suffering from an illness or from a finite set of illnesses. However, there are many situations in which health professionals need a numerical assessment to quantify the severity of an illness, thus dealing with a regression problem instead. Proposals using Feature Selection here are very limited. This paper examines several Feature Selection techniques to gauge their applicability to the regression-type problems, comparing these techniques by applying them to a real-life scenario on the functional profiles of older adults. Data from 829 functional profiles assessments in 49 residential homes were used in this study. The number of features was reduced from 31 to 25—with a correlation between inputs and outputs of 0.99 according to the R2 score and a Mean Square Error (MSE) of 0.11—or to 14 features—with a correlation of 0.98 and MSE of 5.73. |
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Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profileaging informaticsehealthFeature selectionregressionhealthcare data analyticmachine learningHealthcare systems are capable of collecting a significant number of patient health-related parameters. Analyzing them to find the reasons that cause a given disease is challenging. Feature Selection techniques have been used to address this issue—reducing these parameters to a smaller set with the most ”determinant” information. However, existing proposals usually focus on classification problems—aimed to detect whether a person is or is not suffering from an illness or from a finite set of illnesses. However, there are many situations in which health professionals need a numerical assessment to quantify the severity of an illness, thus dealing with a regression problem instead. Proposals using Feature Selection here are very limited. This paper examines several Feature Selection techniques to gauge their applicability to the regression-type problems, comparing these techniques by applying them to a real-life scenario on the functional profiles of older adults. Data from 829 functional profiles assessments in 49 residential homes were used in this study. The number of features was reduced from 31 to 25—with a correlation between inputs and outputs of 0.99 according to the R2 score and a Mean Square Error (MSE) of 0.11—or to 14 features—with a correlation of 0.98 and MSE of 5.73.IEEE Transactions on Emerging Topics in Computing2022-08-31T11:16:37Z2022-08-312022-06-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/32472https://doi.org/Rojo, J., Pinho, L.G., Fonseca, C., Lopes, M.J., Helal, A., Hernández, J., Murillo, J., Garcia-Alonso, J. (2022). Analyzing the Performance of Feature Selection on Regression Problems: a Case Study on Older Adults' Functional Profile. IEEE Transactions on Emerging Topics in Computing. https://doi.org/10.1109/TETC.2022.3181679https://doi.org/10.1109/TETC.2022.3181679http://hdl.handle.net/10174/32472https://doi.org/10.1109/TETC.2022.3181679porndlmgp@uevora.ptcfonseca@uevora.ptmjl@uevora.ptndndndndRojo, JavierPinho, LaraFonseca, CésarLopes, ManuelHelal, SumiHernández, JuanGarcia-Alonso, JoseMurillo, Juan Manuelinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:33:00Zoai:dspace.uevora.pt:10174/32472Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:21:23.354822Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile |
title |
Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile |
spellingShingle |
Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile Rojo, Javier aging informatics ehealth Feature selection regression healthcare data analytic machine learning |
title_short |
Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile |
title_full |
Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile |
title_fullStr |
Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile |
title_full_unstemmed |
Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile |
title_sort |
Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile |
author |
Rojo, Javier |
author_facet |
Rojo, Javier Pinho, Lara Fonseca, César Lopes, Manuel Helal, Sumi Hernández, Juan Garcia-Alonso, Jose Murillo, Juan Manuel |
author_role |
author |
author2 |
Pinho, Lara Fonseca, César Lopes, Manuel Helal, Sumi Hernández, Juan Garcia-Alonso, Jose Murillo, Juan Manuel |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Rojo, Javier Pinho, Lara Fonseca, César Lopes, Manuel Helal, Sumi Hernández, Juan Garcia-Alonso, Jose Murillo, Juan Manuel |
dc.subject.por.fl_str_mv |
aging informatics ehealth Feature selection regression healthcare data analytic machine learning |
topic |
aging informatics ehealth Feature selection regression healthcare data analytic machine learning |
description |
Healthcare systems are capable of collecting a significant number of patient health-related parameters. Analyzing them to find the reasons that cause a given disease is challenging. Feature Selection techniques have been used to address this issue—reducing these parameters to a smaller set with the most ”determinant” information. However, existing proposals usually focus on classification problems—aimed to detect whether a person is or is not suffering from an illness or from a finite set of illnesses. However, there are many situations in which health professionals need a numerical assessment to quantify the severity of an illness, thus dealing with a regression problem instead. Proposals using Feature Selection here are very limited. This paper examines several Feature Selection techniques to gauge their applicability to the regression-type problems, comparing these techniques by applying them to a real-life scenario on the functional profiles of older adults. Data from 829 functional profiles assessments in 49 residential homes were used in this study. The number of features was reduced from 31 to 25—with a correlation between inputs and outputs of 0.99 according to the R2 score and a Mean Square Error (MSE) of 0.11—or to 14 features—with a correlation of 0.98 and MSE of 5.73. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-08-31T11:16:37Z 2022-08-31 2022-06-17T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/32472 https://doi.org/Rojo, J., Pinho, L.G., Fonseca, C., Lopes, M.J., Helal, A., Hernández, J., Murillo, J., Garcia-Alonso, J. (2022). Analyzing the Performance of Feature Selection on Regression Problems: a Case Study on Older Adults' Functional Profile. IEEE Transactions on Emerging Topics in Computing. https://doi.org/10.1109/TETC.2022.3181679 https://doi.org/10.1109/TETC.2022.3181679 http://hdl.handle.net/10174/32472 https://doi.org/10.1109/TETC.2022.3181679 |
url |
http://hdl.handle.net/10174/32472 https://doi.org/Rojo, J., Pinho, L.G., Fonseca, C., Lopes, M.J., Helal, A., Hernández, J., Murillo, J., Garcia-Alonso, J. (2022). Analyzing the Performance of Feature Selection on Regression Problems: a Case Study on Older Adults' Functional Profile. IEEE Transactions on Emerging Topics in Computing. https://doi.org/10.1109/TETC.2022.3181679 https://doi.org/10.1109/TETC.2022.3181679 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
nd lmgp@uevora.pt cfonseca@uevora.pt mjl@uevora.pt nd nd nd nd |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
IEEE Transactions on Emerging Topics in Computing |
publisher.none.fl_str_mv |
IEEE Transactions on Emerging Topics in Computing |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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