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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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