Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings a...

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Autores: Mahendran, Nivedhitha, Vincent, Durai Raj, Srinivasan, Kathiravan, Chang, Chuan-Yu, Garg, Akhil, Gao, Liang, Gutiérrez Reina, Daniel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/92987
Acceso en línea:https://hdl.handle.net/11441/92987
Access Level:acceso abierto
Palabra clave:Correlation-based feature selection
Random forest
Weighted average ensemble
Major depressive disorder
Smartwatch sensor
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spelling Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive DisorderMahendran, NivedhithaVincent, Durai RajSrinivasan, KathiravanChang, Chuan-YuGarg, AkhilGao, LiangGutiérrez Reina, DanielCorrelation-based feature selectionRandom forestWeighted average ensembleMajor depressive disorderSmartwatch sensorThe present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches.MDPIIngeniería Electrónica2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/92987reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSensors, 19 (22). Article number 4822.https://doi.org/10.3390/s19224822info:eu-repo/semantics/openAccessoai:idus.us.es:11441/929872026-06-17T12:51:07Z
dc.title.none.fl_str_mv Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
title Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
spellingShingle Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
Mahendran, Nivedhitha
Correlation-based feature selection
Random forest
Weighted average ensemble
Major depressive disorder
Smartwatch sensor
title_short Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
title_full Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
title_fullStr Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
title_full_unstemmed Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
title_sort Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
dc.creator.none.fl_str_mv Mahendran, Nivedhitha
Vincent, Durai Raj
Srinivasan, Kathiravan
Chang, Chuan-Yu
Garg, Akhil
Gao, Liang
Gutiérrez Reina, Daniel
author Mahendran, Nivedhitha
author_facet Mahendran, Nivedhitha
Vincent, Durai Raj
Srinivasan, Kathiravan
Chang, Chuan-Yu
Garg, Akhil
Gao, Liang
Gutiérrez Reina, Daniel
author_role author
author2 Vincent, Durai Raj
Srinivasan, Kathiravan
Chang, Chuan-Yu
Garg, Akhil
Gao, Liang
Gutiérrez Reina, Daniel
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Electrónica
dc.subject.none.fl_str_mv Correlation-based feature selection
Random forest
Weighted average ensemble
Major depressive disorder
Smartwatch sensor
topic Correlation-based feature selection
Random forest
Weighted average ensemble
Major depressive disorder
Smartwatch sensor
description The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/92987
url https://hdl.handle.net/11441/92987
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors, 19 (22). Article number 4822.
https://doi.org/10.3390/s19224822
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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