Sensor-assisted weighted average ensemble model for detecting major depressive disorder
Open accessAbstract
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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Mahendran et al. found that an ensemble of logistic regression and random forest models performed better than individual models, with random forest outperforming logistic regression.
“The results indicated that the weighted average ensemble performed better than the individual models, with random forest outperforming logistic regression”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [35]
The integration of movement, light exposure, and smartphone data has enabled accurate predictions and revealed common features across mood disorders, indicating transdiagnostic traits.
“The integration of movement, light exposure, and smartphone data has not only enabled accurate predictions but has also revealed common features across mood disorders, highlighting the existence of transdiagnostic traits.”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [35]
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References 44 · Cited by 51
- Authors
- Mahendran, N., Vincent, D. R., Srinivasan, K., Chang, C.-Y., Garg, A., Gao, L., & Reina, D. G.
- Affiliations
- Vellore Institute of Technology University (IN)
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
- National Yunlin University of Science and Technology (TW)
- State Key Laboratory of Digital Manufacturing Equipment and Technology (CN)
- Universidad de Sevilla (ES)
- Corresponding author
- Chang, Chuan-Yu
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-11-06
- Volume
- 19
- Issue
- 22
- Pages
- 4822
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6891280
- OpenAlex
- W2988068973
- Cited by
- 51
- References
- 44
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 5 · 2023: 9 · 2022: 7 · 2021: 8 · 2020: 3 · 2019: 1
- FWCI
- 5.18
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- idUS (Universidad de Sevilla) (submittedVersion, cc-by-nc-nd)
- Funders
- Ministry of Education, Taiwan (108N04-2)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- major depressive disorder, depression detection, weighted average ensemble, smartwatch data, wearable sensors, self-ratings, feature selection, correlation-based feature selection, ensemble learning, random forest, logistic regression
- MeSH terms
- Machine Learning, Adult, Major Depressive Disorder, Female, Humans, Male, Logistic Models, Support Vector Machine
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