Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importance

The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis and help in the prescription of preventive measures. In parti...

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Autores: López Ibáñez, Beatriz, Torrent-Fontbona, Ferran, Viñas, Ramon, Fernández-Real Lemos, José Manuel
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2018
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/14506
Acesso em linha:http://hdl.handle.net/10256/14506
Access Level:acceso abierto
Palavra-chave:Diabetis no-insulinodependent.
Non-insulin-dependent diabetes.
Diàtesi
Disease susceptibility
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
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spelling Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importanceLópez Ibáñez, BeatrizTorrent-Fontbona, FerranViñas, RamonFernández-Real Lemos, José ManuelDiabetis no-insulinodependent.Non-insulin-dependent diabetes.DiàtesiDisease susceptibilityIntel·ligència artificial -- Aplicacions a la medicinaArtificial intelligence -- Medical applicationsThe use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis and help in the prescription of preventive measures. In particular, the aim is to help physicians to identify the relevant SNPs related to Type 2 diabetes, and to build a decision-support tool for risk prediction. Methods: We use the Random Forest (RF) technique in order to search for the most important attributes (SNPs) related to diabetes, giving a weight (degree of importance), ranging between 0 and 1, to each attribute. Support Vector Machines and Logistic Regression have also been used since they are two other machine learning techniques that are well-established in the health community. Their performance has been compared to that achieved by RF. Furthermore, the relevance of the attributes obtained through the use of RF has then been used to perform predictions with k-Nearest Neighbour method weighting attributes in the similarity measure according to the relevance of the attributes with RF. Results: Testing is performed on a set of 677 subjects. RF is able to handle the complexity of features' interactions, overfitting, and unknown attribute values, providing the SNPs' relevance with an up to 0.89 area under the ROC curve in terms of risk prediction. RF outperforms all the other tested machine learning techniques in terms of prediction accuracy, and in terms of the stability of the estimated relevance of the attributes. Conclusions: The Random Forest is a useful method for learning predictive models and the relevance of SNPs without any underlying assumptionThis work was supported by the European Unions Horizon 2020 research and innovation programme [grant number 689810, PEPPER]; the University of Girona [grant number MPCUdG2016]; and the Spanish MINECO [grant number DPI2013-47450-C21-R].ElsevierMinisterio de Economía y Competitividad (Espanya)info2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/14506http://hdl.handle.net/10256/14506© Artificial Intelligence in Medicine, 2017, vol. 85, p. 45-49Articles publicats (D-EEEiA)López Ibáñez, Beatriz Torrent-Fontbona, Ferran Viñas, Ramon Fernández-Real Lemos, José Manuel 2017 Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetesRandom ForestFeature learningPredictive modelGini importance Artificial Intelligence in Medicinereponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.artmed.2017.09.005info:eu-repo/semantics/altIdentifier/issn/0933-3657info:eu-repo/semantics/altIdentifier/eissn/1873-2860info:eu-repo/grantAgreement/MINECO//DPI2013-47450-C2-1-Rinfo:eu-repo/grantAgreement/EC/H2020/689810Tots els drets reservatsinfo:eu-repo/semantics/openAccessoai:recercat.cat:10256/145062026-05-29T05:05:01Z
dc.title.none.fl_str_mv Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importance
title Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importance
spellingShingle Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importance
López Ibáñez, Beatriz
Diabetis no-insulinodependent.
Non-insulin-dependent diabetes.
Diàtesi
Disease susceptibility
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
title_short Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importance
title_full Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importance
title_fullStr Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importance
title_full_unstemmed Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importance
title_sort Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes Random Forest Feature learning Predictive model Gini importance
dc.creator.none.fl_str_mv López Ibáñez, Beatriz
Torrent-Fontbona, Ferran
Viñas, Ramon
Fernández-Real Lemos, José Manuel
author López Ibáñez, Beatriz
author_facet López Ibáñez, Beatriz
Torrent-Fontbona, Ferran
Viñas, Ramon
Fernández-Real Lemos, José Manuel
author_role author
author2 Torrent-Fontbona, Ferran
Viñas, Ramon
Fernández-Real Lemos, José Manuel
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (Espanya)
dc.subject.none.fl_str_mv Diabetis no-insulinodependent.
Non-insulin-dependent diabetes.
Diàtesi
Disease susceptibility
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
topic Diabetis no-insulinodependent.
Non-insulin-dependent diabetes.
Diàtesi
Disease susceptibility
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
description The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis and help in the prescription of preventive measures. In particular, the aim is to help physicians to identify the relevant SNPs related to Type 2 diabetes, and to build a decision-support tool for risk prediction. Methods: We use the Random Forest (RF) technique in order to search for the most important attributes (SNPs) related to diabetes, giving a weight (degree of importance), ranging between 0 and 1, to each attribute. Support Vector Machines and Logistic Regression have also been used since they are two other machine learning techniques that are well-established in the health community. Their performance has been compared to that achieved by RF. Furthermore, the relevance of the attributes obtained through the use of RF has then been used to perform predictions with k-Nearest Neighbour method weighting attributes in the similarity measure according to the relevance of the attributes with RF. Results: Testing is performed on a set of 677 subjects. RF is able to handle the complexity of features' interactions, overfitting, and unknown attribute values, providing the SNPs' relevance with an up to 0.89 area under the ROC curve in terms of risk prediction. RF outperforms all the other tested machine learning techniques in terms of prediction accuracy, and in terms of the stability of the estimated relevance of the attributes. Conclusions: The Random Forest is a useful method for learning predictive models and the relevance of SNPs without any underlying assumption
publishDate 2018
dc.date.none.fl_str_mv 2018
info
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
peer-reviewed
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/14506
http://hdl.handle.net/10256/14506
url http://hdl.handle.net/10256/14506
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.artmed.2017.09.005
info:eu-repo/semantics/altIdentifier/issn/0933-3657
info:eu-repo/semantics/altIdentifier/eissn/1873-2860
info:eu-repo/grantAgreement/MINECO//DPI2013-47450-C2-1-R
info:eu-repo/grantAgreement/EC/H2020/689810
dc.rights.none.fl_str_mv Tots els drets reservats
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Tots els drets reservats
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv © Artificial Intelligence in Medicine, 2017, vol. 85, p. 45-49
Articles publicats (D-EEEiA)
López Ibáñez, Beatriz Torrent-Fontbona, Ferran Viñas, Ramon Fernández-Real Lemos, José Manuel 2017 Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetesRandom ForestFeature learningPredictive modelGini importance Artificial Intelligence in Medicine
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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