dc.creator | Mahendran, Nivedhitha | es |
dc.creator | Vincent, Durai Raj | es |
dc.creator | Srinivasan, Kathiravan | es |
dc.creator | Chang, Chuan-Yu | es |
dc.creator | Garg, Akhil | es |
dc.creator | Gao, Liang | es |
dc.creator | Gutiérrez Reina, Daniel | es |
dc.date.accessioned | 2020-02-12T19:48:16Z | |
dc.date.available | 2020-02-12T19:48:16Z | |
dc.date.issued | 2019-11 | |
dc.identifier.citation | Mahendran, N., Vincent, D.R., Srinivasan, K., Chang, C., Garg, A., Gao, L. y Gutiérrez Reina, D. (2019). Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder. Sensors, 19 (22). Article number 4822. | |
dc.identifier.issn | 1424-8220 | es |
dc.identifier.uri | https://hdl.handle.net/11441/92987 | |
dc.description.abstract | 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. | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Sensors, 19 (22). Article number 4822. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Correlation-based feature selection | es |
dc.subject | Random forest | es |
dc.subject | Weighted average ensemble | es |
dc.subject | Major depressive disorder | es |
dc.subject | Smartwatch sensor | es |
dc.title | Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Electrónica | es |
dc.relation.publisherversion | https://doi.org/10.3390/s19224822 | es |
idus.format.extent | 16 p. | es |
dc.journaltitle | Sensors | es |
dc.publication.volumen | 19 | es |
dc.publication.issue | 22 | es |
dc.publication.endPage | Article number 4822 | es |