Machine learning algorithms for depression: diagnosis, insights, and research directions
Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense
psychological effects on people's minds worldwide. The global technological development …
psychological effects on people's minds worldwide. The global technological development …
Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2022 - Elsevier
Depression is one of the significant contributors to the global burden disease, affecting
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
A Comparative Performance Assessment of Optimized Multilevel Ensemble Learning Model with Existing Classifier Models
To predict the class level of any classification problem, predictive models are used and
mostly a single predictive model is built to predict the class level of any classification …
mostly a single predictive model is built to predict the class level of any classification …
Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study
Autism spectrum is a brain development condition that impairs an individual's capacity to
communicate socially and manifests through strict routines and obsessive–compulsive …
communicate socially and manifests through strict routines and obsessive–compulsive …
An insight into diagnosis of depression using machine learning techniques: a systematic review
Background In this modern era, depression is one of the most prevalent mental disorders
from which millions of individuals are affected today. The symptoms of depression are …
from which millions of individuals are affected today. The symptoms of depression are …
Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review
Epidemiological studies report high levels of anxiety and depression amongst adolescents.
These psychiatric conditions and complex interplays of biological, social and environmental …
These psychiatric conditions and complex interplays of biological, social and environmental …
Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample
Abstract Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in
challenges with early detection. However, changes in sleep and movement patterns may …
challenges with early detection. However, changes in sleep and movement patterns may …
Enhancing the efficacy of depression detection system using optimal feature selection from EHR
Diagnosing depression at an early stage is crucial and majorly depends on the clinician's
skill. The present work aims to develop an automated tool for assisting the diagnostic …
skill. The present work aims to develop an automated tool for assisting the diagnostic …
Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine
L Yi, G Xie, Z Li, X Li, Y Zhang, K Wu, G Shao… - Frontiers in …, 2023 - frontiersin.org
Depression is a common mental disorder that seriously affects patients' social function and
daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this …
daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this …
A review of overfitting solutions in smart depression detection models
GK Gupta, DK Sharma - 2022 9th International Conference on …, 2022 - ieeexplore.ieee.org
Overfitting is a common issue in machine learning-based depression detection model.
Overfitting occurs when a machine learning model uses garbage data in the training …
Overfitting occurs when a machine learning model uses garbage data in the training …