Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms

Abstract

Background: Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA).Objective: This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality. Material and Methods:In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models.Results: A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively. Conclusion:The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.

Predictors of mortality in hospitalised patients with COVID-19: a 1-year case–control study

“…In contrast, Afrash et al 41 developed hybrid machine-learning algorithms to predict mortality. Authors found that the mixture of variables such as length of stay (LOS), age, cough, respiratory intubation, dyspnoea, cardiovascular disease, leucocytosis, BUN, CRP and pleural effusion yielded a high accuracy (90%), specificity (83%) and sensitivity (97%).…”

“…Several authors have developed similar approaches involving different variables in the prediction of mortality; some of them include dyspnoea, 41 42 BUN, platelet count, 42 43 sex, 42 age, 41 43–46 cough, 41 weight, 44 cardiovascular disease, 41 orotracheal intubation, 41 and pleural effusion, 41 44 respiratory rate, 42 fraction of inspired oxygen, 42 blood oxygen saturation 42 43 pH, 42 aspartate aminotransferase levels, 42 estimated glomerular filtration rate, 42 lymphocyte count, 44 47 WCC, 41 43 creatine 46 lactic acid 47 and serum calcium. 47 However, most of their studies did not focus on the interaction of such variables on hospital admission, and their interpretability by clinicians is difficult.…”

Does workforce explain the relationship between funding and patient experience? A mediation analysis of primary care data in England

“…In contrast, Afrash et al 41 developed hybrid machinelearning algorithms to predict mortality. Authors found that the mixture of variables such as length of stay (LOS), Open access age, cough, respiratory intubation, dyspnoea, cardiovascular disease, leucocytosis, BUN, CRP and pleural effusion yielded a high accuracy (90%), specificity (83%) and sensitivity (97%).…”

“…Several authors have developed similar approaches involving different variables in the prediction of mortality; some of them include dyspnoea, 41 42 BUN, platelet count, 42 43 sex, 42 age, 41 43-46 cough, 41 weight, 44 cardiovascular disease, 41 orotracheal intubation, 41 and pleural effusion, 41 44 respiratory rate, 42 fraction of inspired oxygen, 42 blood oxygen saturation 42 43 pH, 42 aspartate aminotransferase levels, 42 estimated glomerular filtration rate, 42 lymphocyte count, 44 47 WCC, 41 43 creatine 46 lactic acid 47 and serum calcium. 47 However, most of their studies did not focus on the interaction of such variables on hospital admission, and their interpretability by clinicians is difficult.…”

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study

“…Such a strategy conceives significant acceleration of the triage for selecting critical patients to commence the appropriate treatment immediately and for the optimizing of the workflow within the health facilities [ 18 ]. Accuracy of the predictions made by the corresponding ML tools fluctuates from 81 to 96%, including age, reduced oxygen saturation, increased serum lactate dehydrogenase, C-reactive protein, and impaired kidney function as major predictors of COVID-19-associated death [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Yet, these factors and their relative impact on mortality significantly vary between the countries and hospitals, probably due to distinct treatment protocols [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”