An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. BRBES
Modeling Domain Knowledge Using BRB
3.2. Inference with BRB Using ER Approach
3.2.1. Input Transformation and Individual Matching Degree Calculation
3.2.2. Calculation of Activation Weight of Each Belief Rule
3.2.3. Construction of BRB Expression Matrix
3.2.4. Integration of Activated Belief Rule Using ER Algorithm
3.3. Training BRB with Historical Data
3.4. The Modified DE Algorithm
Algorithm 1: Optimization of BRB with Modified DE. |
|
4. Experiments
4.1. Dataset
4.2. Experimental Settings
5. Result and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
BRBES | Belief Rule-Based Expert System |
CBRB | Conjunctive Belief Rule Base |
DBRB | Disjunctive Belief Rule Base |
CDSS | Clinical Decision Support System |
DE | Differential Evolution |
SVM | Support Vector Machine |
DT | Decision Tree |
ANN | Artificial Neural Network |
LR | Logistic Regression |
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Ref. No. | Method | Limitations |
---|---|---|
[4] | Logistic Regression, K Nearest Neighbor, Random Forest, Support Vector Machine Decision Tree | Accuracy is low, Cannot integrate expert knowledge in the prediction process, Dataset is small and imbalance |
[26] | Logistic Regression, Gradient Boosting Tree, Random Forest, Support Vector Machine Neural Network | Accuracy is low, Cannot integrate expert knowledge in the prediction process Dataset is small and imbalance |
[27] | Logistic Regression, eXtreme Gradient Boosting, Random Forest, Support Vector Machine Neural Network | Accuracy is low, Cannot integrate expert knowledge in the prediction process Dataset is small and imbalance |
[28] | Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, Recurrent Neural Networks CNNLSTM, and CNNRNN | Can not integrate expert knowledge in the prediction process Dataset is small and imbalance |
[29] | Machine Learning based radiomic model using logistic regression | Sensitive to inter-observer classifications, Can not be used without any caution |
[30] | Gini impurity for feature selection Random forest for classification | Based on many biochemistry features which are costly to collect from lab test |
[31] | Logistic Regression, Adaptive Boosting, Random Forest, Support Vector Machine K nearest neighbor | Accuracy is low, Require around 52 features for prediction. Therefore, not suitable for practical application |
Activation Weight | Consequrnt Belief Degree | |||
---|---|---|---|---|
… | ||||
… | ||||
… | ||||
… | … | … | … | … |
… | ||||
… | … | … | … | … |
… |
Predicted Critical | Predicted Non-Critical | |
---|---|---|
Actual Critical | TP | FN |
Actual non-critical | FP | TN |
Referential Points | Very Low (VL) | Low (L) | Medium (M) | High (H) | Very High (VH) |
---|---|---|---|---|---|
Numerical Values | 122 | 558.25 | 994.5 | 1430.75 | 1867 |
Referential Points | Very Low (VL) | Low (L) | Medium (M) | High (L) | Very High (VH) |
---|---|---|---|---|---|
Numerical Values | 0 | 12.5 | 25 | 37.5 | 50 |
Referential Points | Very Low (VL) | Low (L) | Medium (M) | High (H) | Very High (VH) |
---|---|---|---|---|---|
Numerical Values | 0 | 79.5 | 159 | 238.5 | 318 |
Rule Weight | Antecedent Attributes | COVID-19 Clinical Severity | |||
---|---|---|---|---|---|
A1 | A2 | A3 | Normal | Critical | |
0.9 | VH | VH | VH | 0.0 | 1.0 |
0.9 | VH | VH | H | 0.1 | 0.9 |
… | … | … | … | … | … |
1.0 | M | M | M | 0.5 | 0.5 |
… | … | … | … | … | … |
0.9 | VL | VL | M | 0.9 | 0.1 |
1.0 | VL | VL | VL | 1.0 | 0.0 |
Rule Weight | Antecedent Attributes | COVID-19 Clinical Severity | |||
---|---|---|---|---|---|
A1 | A2 | A3 | Normal | Critical | |
0.8 | VH | VH | VH | 0.0 | 1.0 |
0.9 | H | H | H | 0.3 | 0.7 |
0.8 | M | M | M | 0.5 | 0.5 |
0.9 | L | L | L | 0.7 | 0.3 |
0.7 | VL | VL | VL | 1.0 | 0.0 |
Model | Settings |
---|---|
Logistic Regression (LR) | Loss: binary cross-entropy; penalty: l2 |
Optimizer: lbfgs; learning rate: 0.001 | |
Support Vector Machine (SVM) | Kernel: linear; C: 1.25 |
Decision Tree (DT) | Splitting Criteria: Gini-index; Maximum Depth: 3 |
Neural Network (NN) | First hidden layer—units: 10; activation: relu |
Second hidden layer—units: 5; activation: relu | |
Output layer—unit: 1; activation: sigmoid | |
Loss: binary cross-entropy; optimizer: adam; learning rate: 0.001 |
P-CBRB | T-CBRB | P-DBRB | T-DBRB | |
---|---|---|---|---|
Accuracy | 0.845 | 0.954 | 0.791 | 0.927 |
Sensitivity | 0.615 | 0.923 | 0.538 | 0.769 |
Specificity | 0.876 | 0.959 | 0.825 | 0.948 |
No of Parameter | 378 | 18 |
BRBES | LR | SVM | NN | DT | |
---|---|---|---|---|---|
Accuracy | 0.954 | 0.855 | 0.873 | 0.891 | 0.918 |
Sensitivity | 0.923 | 0.615 | 0.692 | 0.769 | 0.846 |
Specificity | 0.959 | 0.887 | 0.897 | 0.907 | 0.928 |
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Ahmed, F.; Hossain, M.S.; Islam, R.U.; Andersson, K. An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty. Appl. Sci. 2021, 11, 5810. https://doi.org/10.3390/app11135810
Ahmed F, Hossain MS, Islam RU, Andersson K. An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty. Applied Sciences. 2021; 11(13):5810. https://doi.org/10.3390/app11135810
Chicago/Turabian StyleAhmed, Faisal, Mohammad Shahadat Hossain, Raihan Ul Islam, and Karl Andersson. 2021. "An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty" Applied Sciences 11, no. 13: 5810. https://doi.org/10.3390/app11135810
APA StyleAhmed, F., Hossain, M. S., Islam, R. U., & Andersson, K. (2021). An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty. Applied Sciences, 11(13), 5810. https://doi.org/10.3390/app11135810