ArticleBrain informatics2021
Detecting depression using an ensemble classifier based on Quality of Life scales.
Article in Brain informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 52 citations in OpenAlex.
- The effect of the Yara smartphone application on anxiety, sleep quality, and suicidal thoughts in patients with major depressive disorder in Iran: a randomized controlled trial.BMC psychiatry · 2024Trial
- Machine Learning and Deep Learning for Early Disease Detection Across Modalities: A Comprehensive Review of Methods, Applications, and Clinical Translation.Diagnostics (Basel, Switzerland) · 2026Review
- Translation and psychometric properties of the Persian version of the mental health quality of life (MHQoL) questionnaire.BMC psychiatry · 2025Article
- Antidepressant Effect and Mechanism ofJournal of microbiology and biotechnology · 2024Article
- Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches.The American journal of tropical medicine and hygiene · 2024Article
- Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample.Psychiatry research · 2024Article
- Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data.Translational psychiatry · 2023Article
- Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students.Bioengineering (Basel, Switzerland) · 2023Article
- Prediction of Attention-Deficit/Hyperactivity Disorder Diagnosis Using Brief, Low-Cost Clinical Measures: A Competitive Model Evaluation.Clinical psychological science : a journal of the Association for Psychological Science · 2023Article
- Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine.Frontiers in neuroscience · 2023Article
- Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review.Cognitive neurodynamics · 2022Review
- Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study.Brain informatics · 2022Article
- Prediction Modeling of Mental Well-Being Using Health Behavior Data of College Students.Research square · 2022Article
- Predicting symptom response and engagement in a digital intervention among individuals with schizophrenia and related psychoses.Frontiers in psychiatry · 2022Article
- Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major depressive disorder (MDD) is an issue that affects 350 million people worldwide. Traditional approaches have been to identify depressive symptoms in datasets, but recently, research is beginning to explore the association between psychosocial factors such as those on the quality of life scale and mental well-being, which will lead to earlier diagnosis and prediction of MDD. In this research, an ensemble binary classifier is proposed to analyse health survey data against ground truth from the SF-20 Quality of Life scales. The classifier aims to improve the performance of machine learning techniques on large datasets and identify depressed cases based on associations between items on the QoL scale and mental illness by increasing predictive performance. On the experimental evaluation on the National Health and Nutrition Examination Survey (NHANES), the classifier demonstrated an F1 score of 0.976 in the prediction, without any incorrectly identified depression instances. Only about 4% of instances had been mistakenly classified into depressed cases, with a significant accuracy of 95.4% comparing to the result from PHQ-9 mental screen inventory. The presented ensemble binary classifier performed comparably better than each baseline algorithm in all measures and all experiments. We trained the ensemble model on the processed NHANES dataset, tested and evaluated the results of its performance against mental screen inventory and discussed the comparable predictions. Finally, we provided future research directions.
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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.