Artículo
Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
Autor/es | Mahendran, Nivedhitha
Vincent, Durai Raj Srinivasan, Kathiravan Chang, Chuan-Yu Garg, Akhil Gao, Liang Gutiérrez Reina, Daniel |
Departamento | Universidad de Sevilla. Departamento de Ingeniería Electrónica |
Fecha de publicación | 2019-11 |
Fecha de depósito | 2020-02-12 |
Publicado en |
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Resumen | 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 ... 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. |
Cita | 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. |
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