Table 1 Comparison of results with previous studies.
Reference | Probelm | Related to covid | Data source | Methodology | Result |
|---|---|---|---|---|---|
Classification of users with anxiety, bipolar disorder etc | ✗ | Classical machine learning algorithms | 89% Accuracy | ||
Examine effect of depression on people’s Twitter language | ✗ | Transformer-based classifiers | 78.9% Accuracy | ||
Predicting mental health status of Reddit users | ✗ | SMHD Dataset from Reddit by41 | Hierarchical attention network | HAN performed better in classification of Depression, ADHD, Anxiety, Biolar when compared with other classifiers as compared to PTSD, Autism, OCD, Schizo, Eating | |
Identification of PTSD among cancer survivors | ✗ | CNN | 91.29% Accuracy | ||
Screening of Twitter users for depression and PTSD | ✗ | Lexical decision lists of n-grams | Average precision in the range of 0.70–0.76 | ||
Identification of depression with Temporal Measures of Emotions | ✗ | Emotion and temporal features with RF | 91.81% Accuracy | ||
Classification of mental illness | ✗ | Traditional machine learning, deep learning and transfer learning | 83% Accuracy with RoBERTa | ||
Prediction of mental illness | ✗ | Classical machine learning algorithms | 0.89 AUC with RF | ||
Identification of PTSD among military personnel | ✗ | Self-reported service exposures and a range of validated self-report measures | Classical machine learning algorithms | 97% Accuracy with RF | |
Prediction of PTSD survivors Northern Ugandan rebel war | ✗ | Counsellors visited residents of the former IDP camps and communities at their homes | RF-CI and LASSO | 77.25% Accuracy with RF-CI | |
Classifying PTSD in US veterans | ✗ | Speech samples from warzone-exposed veterans | RF | 0.954 AUC | |
Assessment of PTSD in military personnel | ✗ | Audio recordings of interviews | GB, DT, NN and boosting | 77% Accuracy | |
This work | Identification of PTSD among Covid survivors | \(\checkmark\) | NB, kNN, SVM, RF | 83.29% Accuracy with SVM |
