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| Author, year | Mental health problem | Sample data set | Machine learning model | Performances | Comments |
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| Greenstein, 2012 [20] | Schizophrenia | (i) 98 childhood-onset schizophrenia
(ii) 99 healthy controls | Random forest | Accuracy: 73.7% | Regional brain measured have been chosen to provide lower resolution compared to the higher resolution voxel-wise measures |
| Jo et al., 2020 [21] | Schizophrenia | (i) 48 schizophrenia patients
(ii) 24 healthy controls | (i) Random forest
(ii) Multinomial naive Bayes
(iii) XGBoost
(iv) Support vector machine | Accuracy:
(i) Random forest: 68.9%
(ii) Multinomial naive Bayes: 66.9%
(iii) XGBoost: 66.3%
(iv) Support vector machine: 58.2% | |
| Yang et al., 2010 [22] | Schizophrenia | (i) 20 schizophrenia patients
(ii) 20 healthy controls | Support vector machine | Accuracy:
(i) 0.82 with functional magnetic resonance imaging
(ii) 0.74 with single nucleotide polymorphism | |
| Srinivasagopalan et al., 2019 [23] | Schizophrenia | (i) 69 schizophrenia patients
(ii) 75 controls | (i) Deep learning
(ii) Support vector machine
(iii) Random forest
(iv) Logistic regression | Accuracy:
(i) Deep learning: 94.44%
(ii) Support vector machine: 82.68%
(iii) Random forest: 83.33%
(iv) Logistic regression: 82.77% | |
| Pläschke et al., 2017 [24] | Schizophrenia | (i) 86 schizophrenia patients
(ii) 84 healthy controls | Support vector machine | Accuracy: 68% | Young-old classification was dependent on all networks and outperformed clinical classification |
| Pinaya et al., 2016 [25] | Schizophrenia | (i) 143 schizophrenia patients
(ii) 83 healthy controls | (i) Deep belief network
(ii) Support vector machine | Accuracy:
(i) Deep belief network: 73.6%
(ii) Support vector machine: 68.1% | |
| Chekroud et al., 2016 [27] | Depression | 1949 patients with level 1 of depression | Gradient boosting | Accuracy: 64.6% | |
| Sau and Bhakta, 2017 [29] | Depression and anxiety | 510 elderly patients | (i) Bayesian network
(ii) Naive Bayes
(iii) Logistic regression
(iv) Multilayer perceptron
(v) Sequential minimal optimisation
(vi) K-star
(vii) Random subspace
(viii) J48
(ix) Random forest
(x) Random tree | Accuracy:
(i) Bayesian network: 79.8%
(ii) Naïve Bayes: 79.6%
(iii) Logistic Regression: 72.4%
(iv) Multilayer perceptron: 77.8%
(v) Sequential minimal optimisation: 75.3%
(vi) K-star: 75.3%
(vii) Random subspace: 87.5%
(viii) J48: 87.8%
(ix) Random forest: 89.0%
(x) Random tree: 85.1% | The random forest model has been tested with another data set and achieved the accuracy of 91.0% |
| Ahmed et al., 2019 [28] | Depression and anxiety | Data set of depression and anxiety | (i) Convolutional neural network
(ii) Support vector machine
(iii) Linear discriminant analysis
(iv) K-nearest neighbour | Highest accuracy achieved by convolutional neural network: 96.0% for anxiety and 96.8% for depression | |
| Katsis et al., 2011 [30] | Anxiety | Physiological signals among anxiety patients | (i) Artificial neuro networks
(ii) Random forest
(iii) Neuro-fuzzy system
(iv) Support vector machine | Accuracy:
(i) Artificial neuro networks: 77.3%
(ii) Random forest: 80.83%
(iii) Neuro-fuzzy system: 84.3%
(iv) Support vector machine: 78.5% | Overall classification accuracy is 84.3% |
| Sau and Bhakta, 2019 [31] | Depression and anxiety | Data set of 470 seafarers | (i) CatBoost
(ii) Logistic regression
(iii) Support vector machine
(iv) Naive Bayes
(v) Random forest | Accuracy:
(i) CatBoost: 89.3%
(ii) Logistic regression: 87.5%
(iii) Support vector machine: 87.5%
(iv) Naive Bayes: 82.1%
(v) Random forest: 78.6% | CatBoost has achieved the highest accuracy of 89.3% and highest precision of 89.0% |
| Hilbert et al., 2017 [32] | Anxiety | Multimodal behavioural data with sample of anxiety disorders, healthy persons and major depression | Support vector machine | Accuracy:
(i) 90.10% for the case classification
(ii) 67.46% for the disorder classification | |
| Jerry et al., 2019 [33] | Depression | Text and audio data sets | (i) Gaussian process classification
(ii) Logistic regression
(iii) Neural networks
(iv) Random forest
(v) Support vector machine
(vi) XGBoost
(vii) K-nearest neighbours | Mean F1-score for text data set:
(i) Gaussian process classification: 0.71
(ii) Logistic regression: 0.69
(iii) Neural networks: 0.68
(iv) Random forest: 0.73
(v) Support vector machine: 0.72
(vi) XGBoost: 0.69
(vii) K-nearest neighbours: 0.67
Mean F1-score for audio data set:
(i) Gaussian process classification: 0.48
(ii) Logistic regression: 0.48
(iii) Neural networks: 0.42
(iv) Random forest: 0.44
(v) Support vector machine: 0.40
(vi) XGBoost: 0.50
(vii) K-nearest neighbours: 0.49 | For the text data set, random forests show the best performance. For the audio data set, XGBoost show the best performance. |
| Rocha-Rego et al., 2014 [34] | Bipolar disorder | (i) 40 subjects with bipolar disorder
(ii) 40 subjects of healthy controls | Gaussian process classification | Accuracy: 69–78%
Sensitivity: 64–77%
Specificity: 69–99% | |
| Grotegerd et al., 2013 [35] | Bipolar disorder | (i) 10 subjects with bipolar disorder
(ii) 10 subjects with unipolar disorder
(iii) 10 healthy controls | (i) Gaussian process classification
(ii) Support vector machine | Accuracy:
(i) Gaussian process classification: 70%
(ii) Support vector machine: 70% | |
| Valenza et al., 2016 [36] | Bipolar disorder | Electrocardiogram signals from the patients | Support vector machine | Accuracy: 69% | |
| Mourão-Miranda et al., 2012 [37] | Bipolar disorder | (i) 18 subjects with bipolar disorder
(ii) 18 subjects with unipolar disorder
(iii) 18 healthy controls | Gaussian process classification | Accuracy: 67%
Specificity: 72%
Sensitivity: 61% | |
| Roberts et al., 2016 [38] | Bipolar disorder | (i) 49 bipolar disorder patients
(ii) 71 at-risk subjects
(iii) 80 healthy controls | Multiclass support vector machine | Overall accuracy: 64.3% | |
| Akinci et al., 2012 [39] | Bipolar disorder | (i) 40 subjects with bipolar disorder
(ii) 55 healthy controls | Support vector machine | Accuracy: 96.36% | |
| Wu et al., 2016 [40] | Bipolar disorder | (i) 21 subjects of bipolar disorder
(ii) 21 healthy controls | LASSO | Accuracy: 71%
AUC: 0.714 | |
| Reece et al., 2017 [41] | PTSD | (i) 63 PTSD
(ii) 111 healthy controls | Random forest | AUC: 0.89 | There are two categories for detecting the PTSD and depression |
| Leightley et al., 2018 [42] | PTSD | 13,690 subjects of the military forces from 2004 to 2009 | (i) Support vector machine
(ii) Random Forest
(iii) Artificial neural networks
(iv) Bagging | Accuracy:
(i) Support vector machine: 91%
(ii) Random forest: 97%
(iii) Artificial neural networks: 89%
(iv) Bagging: 95% | Exploitation of alcohol, gender, and deployment status are the variables affected to the performance |
| Papini et al., 2018 [43] | PTSD | (i) 110 PTSD patients
(ii) 231 trauma-exposed controls | Gradient-boosted decision trees | Accuracy: 78%
AUC: 0.85
Sensitivity: 69%
Specificity: 83% | |
| Conrad et al., 2017 [44] | PTSD | (i) 441 trauma-exposed subjects as training data set
(ii) 211 trauma-exposed subjects as testing data set | (i) Random forest with conditional inference
(ii) LASSO
(iii) Linear regression | Accuracy:
(i) Random forest with conditional inference: 77.25%
(ii) LASSO: 74.88%
(iii) Linear regression: 75.36% | |
| Marmar et al., 2019 [45] | PTSD | (i) 52 subjects of PTSD
(ii) 77 trauma-exposed controls | Random forest | Accuracy: 89.1%
AUC: 0.954 | |
| Vergyri et al., 2015 [46] | PTSD | (i) 15 subjects of PTSD
(ii) 24 trauma-exposed controls | (i) Gaussian backend
(ii) Decision tree
(iii) Neural network
(iv) Boosting | Overall accuracy: 77% | Speech features have particular power for the prediction of the PTSD |
| Salminen et al., 2019 [47] | PTSD | 97 war veterans | Support vector machine | Accuracy: 69%
Sensitivity: 58%
Specificity: 81% | The important feature chosen is the surface area in the right posterior cingulate |
| Rangaprakash et al., 2017 [48] | PTSD | 87 male soldiers | Support vector machine | Accuracy: 83.59% | PTSD is associated with hippocampal-striatal hyperconnectivity |
| Sumathi and Poorna, 2016 [49] | Mental health problems among children | Data set from interviews which contained 60 instances | (i) Average one-dependence estimator (AODE)
(ii) Multilayer perceptron
(iii) Logical analysis tree (LAT)
(iv) Radial basis function network (RBFN)
(v) K-star
(vi) Functional tree (FT) | Accuracy:
(i) AODE: 71%
(ii) Multilayer perceptron: 78%
(iii) LAT: 70%
(iv) RBFN: 57%
(v) K-star: 42%
(vi) FT: 42% | The highest accuracy is achieved by multilayer perceptron, which is 78% |
| Tate et al., 2020 [50] | Mental health problems among children | 7638 twins from the Child and Adolescent Twin Study in Sweden | (i) Random forest
(ii) Support vector machine
(iii) Neural network
(iv) Logistic regression
(v) XGBoost | AUC:
(i) Random forest: 0.739
(ii) Support vector machine: 0.736
(iii) Neural network: 0.705
(iv) Logistic regression: 0.700
(v) XGBoost: 0.692 | The performances of machine learning models are close to each other |
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