ArticleSensors (Basel, Switzerland)2019
Sensor-Assisted Weighted Average Ensemble Model for Detecting Major Depressive Disorder.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A Novel Self-Attention Mechanism-Based Dynamic Ensemble Model for Soil Hyperspectral Prediction.Sensors (Basel, Switzerland) · 2025Article
- Deep-kidney: an effective deep learning framework for chronic kidney disease prediction.Health information science and systems · 2024Article
- Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression.NPJ digital medicine · 2023Article
- Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.Journal of medical Internet research · 2023Article
- A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders.Sensors (Basel, Switzerland) · 2023Article
- A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms.Frontiers in psychiatry · 2023Review
- OWAE-Net: COVID-19 detection from ECG images using deep learning and optimized weighted average ensemble technique.Intelligent systems with applications · 2022Article
- Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images.Frontiers in oncology · 2022Article
- Emerging Sensing Technologies in Consumer Electronics.Sensors (Basel, Switzerland) · 2021Article
- Coresets for the Average Case Error for Finite Query Sets.Sensors (Basel, Switzerland) · 2021Article
- Review
- Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities.Sensors (Basel, Switzerland) · 2021Article
- Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times.Computational intelligence and neuroscience · 2021Article
- Design of a Human Evaluator Model for the Ride Comfort of Vehicle on a Speed Bump Using a Neural Artistic Style Extraction.Sensors (Basel, Switzerland) · 2019Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.