Sensor-Assisted Weighted Average Ensemble Model for Detecting Major Depressive Disorder
Nivedhitha Mahendran,
Durai Raj Vincent,
Kathiravan Srinivasan,
Chuan-Yu Chang,
Akhil Garg,
Liang Gao,
Daniel Gutiérrez Reina
Affiliations
Nivedhitha Mahendran
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Durai Raj Vincent
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Kathiravan Srinivasan
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Chuan-Yu Chang
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
Akhil Garg
State Key Lab of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Liang Gao
State Key Lab of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Daniel Gutiérrez Reina
Electronic Engineering Department, University of Seville, 41092 Seville, Spain
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 proposed Weighted 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.
