A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients

Abstract

Background Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention. Methods This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients’ outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model. Results The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88–0.98) and AUC 0.90 (95% CI 0.85–0.96) for classic regression models, respectively. Conclusions Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients.

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

“…Further, for the testing results, R 2 and R for ANN were 94% and 97%, while the results for MLR were 97% and 98% accuracy. We can present and organize the findings from our predictive comparison in the following way: Regarding the prediction of COVID-19, MLR was superior to ANN, and this result is similar to the findings of [ 6 , 7 , 23 , 24 , 33 , 34 ]. Additionally, ref.…”

“…Modeling the linear relationship between a dependent variable and a set of independent variables is the goal of MLR, a statistical technique. A dependent variable’s value can be predicted given the values of the independent variables [ 24 ].…”

Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China

“…Similarly, Hari predicted the number of deaths caused by COVID-19 pneumonia using a regression model based on the data collected at the Hopkins Data website [ 33 ]. Multiple linear regression prediction models have high predictive accuracy, and Bakhtiarvand’s regression model used to analyze and predict the severity of COVID-19 patients evidenced for this point [ 34 ]. Considering that the data used in our study are linear, the multiple linear regression model should be more suitable for our prediction.…”

Prediction of the COVID-19 Transmission: A Case Study of Pakistan

“…We have fitted two selected count Regression models on suspected, infected, and recovered cases as factors that impact the novel coronavirus disease 2019, (COVID-19) deaths in Pakistan. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model [ 33 ]. During the period under analysis, we provide an overview of the (suspected, infected, recovered and deaths) COVID-19 occurrences observed in different provinces of Pakistan from the period of 26 February 2020 to 31 January 2021.…”