Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Study Population
3.2. Statistical Analysis
3.3. Data Preprocessing
3.3.1. Missing Data Imputation
3.3.2. Balancing the Dataset
3.4. Feature Reduction
3.5. Feature Selection
3.6. Stacking-Based Machine Learning Model
3.7. Development and Validation of Classification Model
4. Results
4.1. Characteristics and Outcomes
4.2. Best Feature Combination for Early Prediction of ICU
4.3. Development and Validation of the Stacking Model
4.4. Individual Feature as ICU Admission Predictor
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Item | ICU | Non-ICU | Total | Method | χ2 = 17.5 | p Value |
---|---|---|---|---|---|---|
Gender | Chi-square test | χ2 = 17.5 | <0.05 * | |||
Male (%) | 57 (57%) | 74 (56.5%) | 131 (57%) | |||
Female (%) | 43 (43%) | 57 (43.5%) | 100 (43%) | |||
Age (years) | Rank-sum test | Z = −6.2 | <0.05 * | |||
N(missing) | 100 (0) | 131 (0) | 231 (0) | |||
Mean ± SD | 61.6 ± 13.8 | 55.9 ± 15.7 | 58.4 ± 15.1 | |||
Q1, Q3 | 53, 70.2 | 44, 68.5 | 48.0, 69.0 | |||
Min, Max | 28, 93 | 20, 94 | 20, 94 | |||
The time between the disease and admission to hospital (Admission 2Hospital) (Days) | Rank-sum test | Z = −6.2 | <0.05 * | |||
N(missing) | 99 (1) | 128 (3) | 227 (4) | |||
Mean ± SD | 7.9 ± 7.45 | 9.1 ± 5.68 | 8.6 ± 6.53 | |||
Q1, Q3 | 4, 8 | 6, 11 | 5.0, 10.0 | |||
Min, Max | 1, 50 | 1, 45 | 1, 50 | |||
C-reactive protein 1(CRP1) (mg/L) | Rank-sum test | Z = −4.34 | <0.05 * | |||
N(missing) | 97 (3) | 128 (3) | 225 (6) | |||
Mean ± SD | 123 ± 67.1 | 78 ± 61.7 | 97 ± 67.9 | |||
Q1, Q3 | 64, 166 | 26, 134 | 40, 157 | |||
Min, Max | 4, 328 | 1, 207 | 1, 328 | |||
International normalized ratio (INR) | Rank-sum test | Z = 6.53 | 0.78 | |||
N(missing) | 86 (14) | 122 (9) | 208 (23) | |||
Mean ± SD | 1.32 ±0.18 | 1.25 ± 0.14 | 1.28 ± 0.16 | |||
Q1, Q3 | 1.17, 1.4 | 1.16, 1.3 | 1.17, 1.36 | |||
Min, max | 1.07,1.92 | 0.98, 1.9 | 0.98, 1.92 | |||
Prothrombin time 1 (PT1) (s) | Rank-sum test | Z = 3.27 | <0.05 * | |||
N(missing) | 86 (14) | 122 (9) | 208 (23) | |||
Mean ± SD | 14.39 ± 1.91 | 13.63 ± 1.52 | 13.95 ± 1.73 | |||
Q1, Q3 | 12.9, 15.3 | 12.7, 14.2 | 12.8, 14.9 | |||
Min, max | 11.7, 20.6 | 10.7, 20.4 | 10.7, 20.6 | |||
Fibrinogen 1 (mg/L) | Rank-sum test | Z = −5.89 | <0.05 * | |||
N(missing) | 68 (32) | 113 (18) | 181 (50) | |||
Mean ± SD | 4.9 ± 1.44 | 4.99 ± 1.25 | 4.9 ± 1.32 | |||
Q1, Q3 | 4.2, 5.3 | 4.18, 5.45 | 4.2, 5.4 | |||
Min, max | 1.2, 11.5 | 2.68, 9.21 | 1.2, 11.5 | |||
Chest CT lung tissue affected (%) | Rank-sum test | Z = −1.11 | <0.05 * | |||
N(missing) | 88 (12) | 110 (21) | 198 (33) | |||
Mean ± SD | 59.9 ± 19 | 46.1 ± 14.1 | 52.2 ± 17.8 | |||
Q1, Q3 | 49.5, 75 | 40, 60 | 40, 60 | |||
Min, max | 24, 92 | 10, 75 | 10, 92 | |||
Platelet count 1 (/L) | Rank-sum test | Z = 4.74 | 0.44 | |||
N(missing) | 100 (0) | 131 (0) | 231 (0) | |||
Mean ± SD | 182 ± 83.2 | 183 ± 68.8 | 183 ± 75.2 | |||
Q1, Q3 | 126, 233 | 138, 216 | 129, 219 | |||
Min, max | 47, 493 | 38, 436 | 38, 493 | |||
Outcome | 100 (43%) | 131 (57%) | 231 (100%) |
Features | Pearson Correlation Coefficient | Chi-Square Test | Recursive Feature Elimination | Total |
---|---|---|---|---|
CRP | 3 | |||
Chest CT lung tissue affected (%) | 3 | |||
Age | 3 | |||
Admission2Hospital | 3 | |||
Fibrinogen | 3 | |||
Platelet Count | 2 | |||
Gender | 2 | |||
PT | 2 | |||
INR | 2 |
Classifier | Overall | Weighted with 95% CI | |||
---|---|---|---|---|---|
Accuracy | Precision | Sensitivity | Specificity | F1-Score | |
Support Vector Machine (SVM) | 61.21 ± 1.99 | 63.17 ± 1.97 | 61.21 ± 1.99 | 63.09 ± 1.97 | 61.29 ± 1.99 |
XGBoost (XGB) | 65.52 ± 1.94 | 65.92 ± 1.93 | 65.52 ± 1.94 | 65.15 ± 1.94 | 65.64 ± 1.94 |
MLP | 71.12 ± 1.85 | 70.98 ± 1.85 | 71.12 ± 1.85 | 69.4 ± 1.88 | 71.02 ± 1.85 |
Logistic Regression (LR) | 71.12 ± 1.85 | 70.92 ± 1.85 | 71.12 ± 1.85 | 68.67 ± 1.89 | 70.86 ± 1.85 |
K-Nearest Neighbors (KNN) | 71.55 ± 1.84 | 71.55 ± 1.84 | 71.55 ± 1.84 | 70.45 ± 1.86 | 71.55 ± 1.84 |
Extra Trees (ET) | 79.74 ± 1.64 | 79.68 ± 1.64 | 79.74 ± 1.64 | 78.11 ± 1.69 | 79.64 ± 1.64 |
Gradient Boosting (GB) | 81.03 ± 1.6 | 80.98 ± 1.6 | 81.04 ± 1.6 | 79.81 ± 1.64 | 80.98 ± 1.6 |
Random Forest (RF) | 82.33 ± 1.56 | 82.33 ± 1.56 | 82.33 ± 1.56 | 80.55 ± 1.61 | 82.2 ± 1.56 |
Stacking model (RF+ GB+ ET) | 84.48 ± 1.48 | 84.45 ± 1.48 | 84.48 ± 1.48 | 83.64 ± 1.51 | 84.47 ± 1.48 |
Classifier | Overall | Weighted with 95% CI | |||
---|---|---|---|---|---|
Accuracy | Precision | Sensitivity | Specificity | F1-Score | |
XGBoost (XGB) | 67.52 ± 2.32 | 67.86 ± 2.32 | 67.52 ± 2.32 | 67.82 ± 2.32 | 67.56 ± 2.32 |
Support Vector Machine (SVM) | 71.97 ± 2.23 | 72.4 ± 2.22 | 71.97 ± 2.23 | 72.42 ± 2.22 | 72 ± 2.23 |
MLP | 77.71 ± 2.07 | 77.92 ± 2.06 | 77.71 ± 2.07 | 77.93 ± 2.06 | 77.73 ± 2.06 |
Gradient Boosting (GB) | 78.98 ± 2.02 | 79.34 ± 2.01 | 78.98 ± 2.02 | 79.4 ± 2.01 | 79 ± 2.02 |
Logistic Regression (LR) | 80.25 ± 1.98 | 80.25 ± 1.98 | 80.25 ± 1.98 | 79.97 ± 1.99 | 80.25 ± 1.98 |
K-Nearest Neighbors (KNN) | 82.17 ± 1.9 | 82.26 ± 1.9 | 82.16 ± 1.9 | 81.45 ± 1.93 | 82.09 ± 1.9 |
Extra Tree (ET) | 82.80 ± 1.87 | 82.90 ± 1.87 | 82.80 ± 1.87 | 82.90 ± 1.87 | 82.82 ± 1.87 |
Random Forest (RF) | 83.44 ± 1.84 | 83.49 ± 1.84 | 83.44 ± 1.84 | 83.45 ± 1.84 | 83.45 ± 1.84 |
Stacking model (RF + ET+ KNN) | 85.35 ± 1.75 | 85.34 ± 1.76 | 85.35 ± 1.75 | 85.11 ± 1.77 | 85.34 ± 1.76 |
(A) | |||||
95% Confidence Interval Results | |||||
Feature | Overall Accuracy | Weighted Precision | Weighted Recall | Weighted Specificity | Weighted F1-Score |
CRP | 71.11 ± 1.85 | 70.56 ± 1.86 | 71.11 ± 1.85 | 71.11 ± 1.85 | 70.58 ± 1.86 |
Chest CT lung tissue affected (%) | 74.43 ± 1.78 | 77.65 ± 1.7 | 74.43 ± 1.78 | 74.43 ± 1.78 | 71.75 ± 1.84 |
Age | 61.01 ± 1.99 | 36.46 ± 1.96 | 61.01 ± 1.99 | 61.01 ± 1.99 | 45.34 ± 2.03 |
Admission2Hospital | 62.41 ± 1.98 | 37.86 ± 1.98 | 62.41 ± 1.98 | 62.41 ± 1.98 | 46.74 ± 2.03 |
Fibrinogen | 65.31 ± 1.98 | 65.62 ± 1.97 | 65.31 ± 1.98 | 65.31 ± 1.98 | 65.47 ± 2.04 |
Platelet Count | 67.01 ± 1.92 | 42.46 ± 2.02 | 67.01 ± 1.92 | 67.01 ± 1.92 | 51.34 ± 2.04 |
Gender | 64.21 ± 1.95 | 61.51 ± 1.98 | 64.21 ± 1.95 | 64.21 ± 1.95 | 62.65 ± 2.04 |
PT | 57.91 ± 1.9 | 43.36 ± 2.02 | 57.91 ± 1.9 | 57.91 ± 1.9 | 52.24 ± 2.04 |
INR | 59.81 ± 2 | 35.26 ± 1.95 | 59.81 ± 2 | 59.81 ± 2 | 44.14 ± 2.02 |
(B) | |||||
95% Confidence Interval Results | |||||
Feature | Overall Accuracy | Weighted Precision | Weighted Recall | Weighted Specificity | Weighted F1-Score |
CRP | 64.29 ± 2.38 | 64.6 ± 2.37 | 64.29 ± 2.38 | 64.29 ± 2.38 | 64.29 ± 2.38 |
Chest CT lung tissue affected (%) | 68.77 ± 2.3 | 71.79 ± 2.23 | 68.77 ± 2.3 | 68.77 ± 2.3 | 67.88 ± 2.32 |
Age | 55.95 ± 2.46 | 56.51 ± 2.46 | 55.95 ± 2.46 | 55.95 ± 2.46 | 55.75 ± 2.46 |
Admission2Hospital | 73.9 ± 2.18 | 73.99 ± 2.18 | 73.9 ± 2.18 | 73.9 ± 2.18 | 73.92 ± 2.18 |
Fibrinogen | 57.24 ± 2.46 | 57.15 ± 2.46 | 57.24 ± 2.46 | 57.24 ± 2.46 | 57.17 ± 2.46 |
Platelet Count | 47.62 ± 2.48 | 47.96 ± 2.48 | 47.62 ± 2.48 | 47.62 ± 2.48 | 47.2 ± 2.48 |
Gender | 52.75 ± 2.48 | 53.19 ± 2.48 | 52.75 ± 2.48 | 52.75 ± 2.48 | 52.57 ± 2.48 |
PT | 51.47 ± 2.48 | 50.64 ± 2.48 | 51.47 ± 2.48 | 51.47 ± 2.48 | 50.43 ± 2.48 |
INR | 53.39 ± 2.48 | 53.68 ± 2.47 | 53.39 ± 2.48 | 53.39 ± 2.48 | 53.37 ± 2.48 |
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Islam, K.R.; Kumar, J.; Tan, T.L.; Reaz, M.B.I.; Rahman, T.; Khandakar, A.; Abbas, T.; Hossain, M.S.A.; Zughaier, S.M.; Chowdhury, M.E.H. Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning. Diagnostics 2022, 12, 2144. https://doi.org/10.3390/diagnostics12092144
Islam KR, Kumar J, Tan TL, Reaz MBI, Rahman T, Khandakar A, Abbas T, Hossain MSA, Zughaier SM, Chowdhury MEH. Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning. Diagnostics. 2022; 12(9):2144. https://doi.org/10.3390/diagnostics12092144
Chicago/Turabian StyleIslam, Khandaker Reajul, Jaya Kumar, Toh Leong Tan, Mamun Bin Ibne Reaz, Tawsifur Rahman, Amith Khandakar, Tariq Abbas, Md. Sakib Abrar Hossain, Susu M. Zughaier, and Muhammad E. H. Chowdhury. 2022. "Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning" Diagnostics 12, no. 9: 2144. https://doi.org/10.3390/diagnostics12092144