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International Journal of Environmental Science and Technology (17351472)22(5)pp. 3051-3062
Effective utilization of data analysis techniques is paramount in addressing the complex challenges presented by environmental issues. These methodologies empower researchers and practitioners to derive meaningful insights from intricate datasets encompassing air quality, biodiversity, climate change, and other pivotal environmental factors. Through the deployment of robust classification models, such as intelligent classifiers, researchers can accurately classify and predict environmental phenomena. This capability holds significant implications for guiding policy decisions, mitigating environmental risks, and devising sustainable solutions to protect our natural resources and ecosystems. Thus, classification models not only deepen our comprehension of environmental dynamics but also empower proactive measures towards achieving environmental sustainability and resilience amidst global challenges. Intelligent classifiers, distinguished by their exceptional capabilities, have demonstrated superior performance compared to other classification models. However, in all developed intelligent classifiers a similar cost/loss function is implemented in the learning processes, which is continuous and works based on the distance between actual and fitted values. Whereas the nature of the classification is discrete. As a result, in this study, a novel cost/loss function is proposed that in contrast to its conventional version is discrete and works based on the direction. In order to explain the process of the suggested methodology, the feed-forward multilayer perceptrons that are among the most famous intelligent classifiers is considered. In this paper, in order to determine the superiority of the proposed model in the domain of environment, it is implemented on some benchmark data sets which is related to air quality. Numerical results indicate that the performance of the proposed model is better than the conventional multilayer perceptrons in whole benchmark data sets. In addition, numerical results clarify that the developed discrete learning-based multilayer perceptron classifier can averagely gain an 87.68% classification rate, which points to more than 9% improvement over its conventional version. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2024.
Multimedia Tools and Applications (13807501)83(32)pp. 78269-78292
Precise diagnosis of benign and malignant breast cancer plays an important role in the effective treatment of breast cancer patients. Several classification models with different characteristics have been developed and used in a wide range of breast cancer domains to improve classification accuracy. Although the classification models differ in different aspects, they all have the same logic in their learning processes and use a continuous distance-based cost function. However, using a continuous distance-based function as a cost function in the learning processes of the traditional classification models is unreasonable or at least insufficient; since the goal function of the classification, is discrete. Hence, developing a discrete cost function for learning the classification problems, due to more consistency, may improve the classification rate; but, it has been neglected in the literature. In this paper, in contrast to all traditional continuous distance-based learning processes, a novel discrete learning-based process is proposed and implemented on a multilayer perceptron to yield a more consistent intelligent classifier. Then, the proposed discrete learning-based multilayer perceptron (DIMLP) is used for breast cancer classification. Empirical results of the breast cancer datasets indicate that the proposed DIMLP model can averagely achieve the classification rate of 94.70%, while the classification rate for the traditional MLP model is only equal to 88.54%. Therefore, the proposed DIMLP can be an appropriate and efficient alternative model for intelligent breast cancer classification, especially when more accurate results and/or a more reasonable model are required. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Applied Soft Computing (15684946)161
Hybridization of classifiers can often yield outperformed performance compared to its best individual component and mostly have more generalization ability. The majority of combined classifiers reported in the literature benefit the parallel or ensemble topology. The performance of such parallel hybrid classifiers significantly depends on their applied weighting approaches and the accuracy strongly relies on the classifiers' weights. In the literature, several different weighting mechanisms have been developed to yield a higher classification rate, which can be generally categorized into three main categories individual classifiers, averaging-based, and optimization-based algorithms. Although these weighting mechanisms have been commonly and frequently used for parallel hybridization, none of them can guarantee that their obtained classification rate will be optimum. In addition, due to the use of iterative procedures, especially by meta-heuristic optimization-based algorithms, their computational time and cost are always unsatisfactory. In this paper, a linear direct optimal weighting (LDOW) approach is proposed in which it can be generally guaranteed that the optimum classification rate will be directly reached. In this way, the proposed approach can achieve the highest classification rate by the desired computational time and cost and both mentioned limitations of currently-used weighting algorithms can be simultaneously lifted. Empirical results of twenty-three different benchmark data sets from six dissimilar domains indicate that the proposed LDOW approach can yield more classification rate in all data sets and can averagely improve 5.36% the performance of its traditional linear direct non-optimal version with the same components. Furthermore, the proposed LDOW approach can averagely improve 6.87% the classification rate of the averaging weighting algorithms that are similar to the proposed algorithm are direct ones. In addition, the proposed model can even improve 2.80% the classification rate of nonlinear intelligent weighting algorithms, on average. Whereas, the proposed approach is a linear model and its complexity and computational cost are significantly lower than these nonlinear intelligent algorithms. Numerical results also indicate that the proposed model can averagely improve 6.08% the classification rate of meta-heuristic-based weighting algorithms. While the proposed model is a direct model and its computational cost is meaningfully lower than these iterative algorithms. Thus, in theory as well as in practice, it can be inferred that the proposed LDOW approach can be an efficient alternative weighting method for parallel hybridization in the classification field. This is particularly relevant when more accurate results are required or for big data situations where computational time and cost are critical factors to consider. © 2024 Elsevier B.V.
Wireless Personal Communications (1572834X)134(2)pp. 1075-1092
Credit scoring or predicting bankruptcy is among the most crucial techniques for identifying high-risk and low-risk credit situations. Accordingly, enhancing the accuracy of bankruptcy prediction methods decreases the risk of inappropriate financial decisions. Also, increasing the accuracy of credit scoring models brings significant benefits such as improved turnover, credit market growth, proper and efficient allocation of financial resources, and sustained improvement of the profits of banks, investors, funds, and governments. Various statistical classification methods have been developed in the literature with different features and characteristics for more accurate bankruptcy prediction. However, despite all appearance differences in statistical classification approaches, they all adhere to a common idea and concept in their training procedures. The basic operation logic in whole-developed statistical classification methods focuses on maximizing a continuous distance-based cost function to yield the highest performance. Despite it being a common and frequently used procedure for classification purposes, it is an unreasonable and inefficient manner to achieve maximum accuracy in a discrete classification field. In this paper, a new discrete direction-based Logistic Regression that is a common statistical classifier method for bankruptcy forecasting is proposed. In the proposed Logistic Regression, in contrast to all traditionally developed statistical classifiers, the compatibility of the cost function and the training procedure is considered. While it can be shown overall that the performance of the presented discrete direction-based classifier will not be inferior to its continuous counterpart, an evaluation of the suggested classifier is conducted to ascertain its superiority. For this purpose, three credit scoring datasets are considered to assess the classification rate of the presented classifier. Empirical outcomes demonstrate that, as pre-expected, in all cases, the model put forward can attain a superior performance compared to conventional alternatives. These findings clearly demonstrated the significant influence of the consistency between the cost function and the training process on the classification capability, a consideration absent in any of the traditional statistical classification procedures. Consequently, the presented Logistic Regression can be considered an efficient alternative for credit scoring purposes to achieve more accurate results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Cognitive Computation (18669964)16(3)pp. 1345-1363
Classification is one of the most well-known data mining branches used in diverse domains and fields. In the literature, many different classification techniques, such as statistical/intelligent, linear/nonlinear, fuzzy/crisp, shallow/deep, and single/hybrid, have been developed to cover data and systems with different characteristics. Intelligent classification approaches, especially deep learning classifiers, due to their unique features to provide accurate and efficient results, have recently attracted a lot of attention. However, in the learning process of the intelligent classifiers, a continuous distance-based cost function is used to estimate the connection weights, though the goal function in classification problems is discrete and using a continuous cost function in their learning process is unreasonable and inefficient. In this paper, a novel discrete learning–based methodology is proposed to estimate the connection weights of intelligent classifiers more accurately. In the proposed learning process, they are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the connection weights are continuously adjusted and step by step near the target. In the present research, the proposed methodology is exemplarily applied to the deep neural network (DNN), which is one of the most recognized deep classification approaches, with a solid mathematical foundation and strong practical results in complex problems. Although the proposed methodology is just implemented on the DNN, it is a general methodology that can be similarly applied to other shallow and deep intelligent classification models. It can be generally demonstrated that the performance of the proposed discrete learning–based DNN (DIDNN) model, due to its consistency property, will not be worse than the conventional ones. The proposed DIDNN model is exemplarily evaluated on some well-known cancer classification benchmarks to illustrate the efficiency of the proposed model. The empirical results indicate that the proposed model outperforms the conventional versions of the selected deep approach in all data sets. Based on the performance analysis, the DIDNN model can improve the performance of the classic version by approximately 3.39%. Therefore, using this technique is an appropriate and effective alternative to conventional DNN-based models for classification purposes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
International Journal of Computational Intelligence Systems (18756891)16(1)
Over the past decades, different classification approaches with different characteristics have been developed to achieve more efficient and accurate results. Although the loss function used in the training procedure is a significant influential factor in the performance of classification models, it has been less considered. In general, in previous research, two main categories of continuous and semi-continuous distance-based loss functions are often applied to estimate the unknown parameters of classification models. Among these, continuous distance-based cost functions are among the most commonly used and most popular loss functions in diverse statistical and intelligent classifiers. In particular, the fundamental of this category of the loss functions is based on the continuous reduction of the distance between the fitted and actual values with the aim of improving the performance of the model. However, since the goal function of classification models belongs to the class of discrete ones, the application of learning procedures based on a continuous distance-based function is not coordinated with the nature of these problems. Consequently, it is theoretically illogical and practically at least inefficient. Accordingly, in order to fill this research gap, the discrete direction-based loss function in the form of mixed-integer programming is proposed in the training procedure of statistical, shallow/deep intelligent classifiers. In this paper, the impact of the loss function type on the classification rate of the classifiers in the energy domain is investigated. For this purpose, the logistic regression (LR), multilayer perceptron (MLP), and deep multilayer perceptron (DMLP), which are respectively among the most widely used statistical, shallow intelligent, and deep learning classifiers, are exemplarily chosen. Numerical outcomes from 13 benchmark energy datasets show that, in all benchmarks, the performances of the discrete direction learning-based classifiers, i.e., discrete learning-based logistic regression (DILR), discrete learning-based multilayer perceptron (DIMLP), and discrete learning-based deep multilayer perceptron (DIDMLP), is higher than its conventional versions. In addition, the proposed DILR, DIMLP, and DIDMLP models can on average yield an 89.88%, 94.53%, and 96.02% classification rate, which indicate a 6.78%, 5.90%, and 4.69% improvement from the classic versions, which only produce an 84.17%, 89.26%, and 91.72% classification rate. Consequently, the discrete direction-based learning methodology can be a more suitable, effective, and valuable alternative for training processes in statistical and shallow/deep intelligent classification models. © 2023, The Author(s).
Journal of Ambient Intelligence and Humanized Computing (18685145)14(3)pp. 2455-2465
Over the years, classification techniques have been widely used in various fields of application. Intelligent models are among the most popular classification techniques, successfully applied in different science fields. Despite widespread use, intelligent classification models have a fundamental flaw in learning, neglected in the literature. Indeed, the learning process of these existing models is based on a continuous distance-based cost function, which conflicts with the discrete nature of the classification problem. In other words, using this type of function for a classification problem with a discrete objective function is irrational or at least not perfectly rational. The current paper proposes a new classification methodology based on a discrete direction learning-based approach to fill this gap. In order to implement the proposed approach, the multi-layer perceptron model, one of the most famous intelligent models, has been used exemplarily. Although it can be theoretically proven that the performance of the discrete direction learning-based multi-layer perceptron is not worse than its classic version, the proposed model, based on several benchmark datasets from the UCI repository, demonstrates its superiority. The competitive results show that the proposed DIMLP approach achieves a 94.43% classification rate which shows a significant improvement compared to the classic MLP model, which can only reach an 82.13% classification rate. Therefore, the proposed discrete direction learning-based learning approach can be a powerful alternative to traditional intelligent classification approaches. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Energy Reports (23524847)9pp. 4861-4871
Energy consumption classification is one of the most widely-used approaches in the energy area that is applied in various applications such as household, commercial, urban, rural, industrial, etc. Energy consumption due to its substantial positive influence on the quality of made decisions, production and distribution management, and cost reduction, has considerable literature. In the classification literature, numerous statistical, intelligence, and hybrid methods have been developed and proposed in order to yield more accurate results. The common point of all these models is that whole of techniques follow a prevalent and repetitive procedure of learning process based on continuous distance-based cost function. While the objective function of classification is discrete. While the mismatch of the continuous cost function in classification problems with the discrete objective function causes them to be illogical or inefficient. Therefore, in this paper, a novel discrete learning process is offered to eliminate the inconsistency between the cost function and the classification objective function. The main difference between the proposed learning methodology rather than conventional versions is its cost function. In the proposed learning methodology, a mismatching function is considered as a cost function, which is dissimilar to previously developed ones, which are continuous functions based on distance, is a discrete function based on direction. In this way, in the proposed learning process, unknown parameters are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the unknown parameters are continuously adjusted and step-by-step near the target. In this paper, deep multilayer neural networks (DMNs) have been exemplarily applied to implement the proposed learning algorithm. Although more consistency of goal and cost functions will logically has not a negative effect on the classification rate of classifiers. However, in this paper, to show the superiority of the proposed discrete deep multilayer neural network (DDMN) over conventional continuous-based classifiers, four energy consumption benchmark data sets have been employed. These data sets include the Tamilnadu Electricity Board Hourly Readings, Tetouan City Energy Consumption, Household Electric Power consumption, and steel industry Energy Consumption. Empirical results indicate that the proposed DDMN model, as pre-expected, can yield better performance in all cases than the DMN model. The DDMN model can achieve a 93.27% classification rate on average and upgrade its classic version approximately by 4.77%. © 2023 The Authors
Artificial Intelligence in Medicine (09333657)139
Classification is one of the most significant subfields of data mining that has been successfully applied to various applications. The literature has expended substantial effort to present more efficient and accurate classification models. Despite the diversity of the proposed models, they were all created using the same methodology, and their learning processes ignored a fundamental issue. In all existing classification model learning processes, a continuous distance-based cost function is optimized to estimate the unknown parameters. The classification problem's objective function is discrete. Consequently, applying a continuous cost function to a classification problem with a discrete objective function is illogical or inefficient. This paper proposes a novel classification methodology utilizing a discrete cost function in the learning process. To this end, one of the most popular intelligent classification models, the multilayer perceptron (MLP), is used to implement the proposed methodology. Theoretically, the classification performance of the proposed discrete learning-based MLP (DIMLP) model is not dissimilar to that of its continuous learning-based counterpart. Nevertheless, in this study, to demonstrate the efficacy of the DIMLP model, it was applied to several breast cancer classification datasets, and its classification rate was compared to that of the conventional continuous learning-based MLP model. The empirical results indicate that the proposed DIMLP model outperforms the MLP model across all datasets. The results demonstrate that the presented DIMLP classification model achieves an average classification rate of 94.70 %, a 6.95 % improvement over the classification rate of the traditional MLP model, which was 88.54 %. Therefore, the classification approach proposed in this study can be utilized as an alternative learning process in intelligent classification methods for medical decision-making and other classification applications, particularly when more accurate results are required. © 2023 Elsevier B.V.
Biomedical Signal Processing and Control (17468108)84
Classification is one of the most frequently used data mining approaches which has been broadly applied in different fields of sciences, such as engineering, finance, energy, environments, transportation, etc., especially medicine, successfully. Over the years, various intelligent modeling techniques with different properties have been proposed to yield more accurate and more efficient classification results. However, in spite of the different appearance of all of the developed models, the same basic methodology is applied to the learning processes. Based on this methodology, a continuous distance-based cost function is considered and optimized for estimating the unknown parameters in the learning procedures. While using a continuous cost function in the classification field in which the goal function is discrete, is unreasonable or at least quite inefficient. In this paper, in contrast to conventional continuous distance-based methodologies, a novel discrete learning-based methodology is proposed for classification purposes. The main difference between the proposed learning methodology rather than conventional versions is its cost function. In the proposed learning methodology, a mismatching function is considered as a cost function, which is dissimilar to previously developed ones, which are continuous functions based on distance, is a discrete function based on direction. In this way, in the proposed learning process, unknown parameters are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the unknown parameters are continuously adjusted and step-by-step near the target. The multilayer perceptron (MLP) which is one of the most widely-used intelligent classification approaches, is exemplarily chosen in order to implement the proposed methodology. Although it can be generally demonstrated that the classification rate of the proposed discrete learning-based MLP (DIMLP) model will not be worse than its conventional continuous learning-based one. However, in order to determine the superiority of the proposed DIMLP model, it is exemplarily evaluated on the heart disease diagnosis benchmark data set and several other medical datasets, and its performance is compared to the classic multilayer perceptron model. Empirical results illustrate that, as pre-expected, the classification rate of the proposed model is higher than its conventional version in all data sets. Obtained results indicate that the proposed DIMLP classifier can yield a 94.27 % classification rate in heart disease diagnosis, which approximately indicates a 9.35 % improvement over the classic version, which can only produce an 86.21 % classification rate. Therefore, the proposed methodology is an appropriate and effective alternative learning process for intelligent classification approaches, especially when more accurate results and/or a more reasonable model are required. © 2023 Elsevier Ltd
Journal of Chemical Information and Modeling (1549960X)63(7)pp. 1935-1946
In recent years, deep learning models have attracted much attention for classification purposes in chemometrics. The popularity of deep learning models in this field comes from their unique features like universal approximation capability with the desired accuracy. Deep learning classifiers use several intelligent processing layers to model mixed, complex, and nonlinear patterns in the underlying data sets, which is why the development of deep learning based models has never been stopped in the chemometrics literature. Despite the variety of deep learning classification models used in this field, they all use a continuous distance-based cost function in their learning processes. Although using a continuous cost function for learning deep classifiers is a common approach, it conflicts with the discrete nature of the classification problem. In fact, applying a continuous cost function for inherently discrete classification problems can reduce the performance of the classification. In this research, a novel discrete learning based classification approach is proposed and implemented on a deep feed-forward neural network as one of the most commonly used deep learning models to develop a different learning process for deep classification models. The basis of the proposed learning approach is maximizing a discrete matching function of the actual and fitted values instead of minimizing a continuous distance-based cost function. The proposed classification approach is evaluated on five benchmark data sets in the chemistry field. The empirical results indicated the superiority of the proposed discrete deep learning approach over its classic continuous form. The results of this study demonstrate the important effect of discrete learning processes on the performances of deep learning classification models. Therefore, the proposed methodology can be a powerful alternative to common classification approaches to analyze chemical data in the chemometrics field. © 2023 American Chemical Society
Soft Computing (14327643)27(13)pp. 8697-8710
Achieving accurate turning point (TP) forecasting techniques can provide investors with tools to make profitable trading decisions, by offering the opportunity to buy = low and sell = high. Forecasting stock time series TP is known to be one of the most important but also extremely challenging issues. The stock TP forecasting problem is a classification problem where the decision to buy or sell a stock has to be made at each point in time. The first step to achieve this goal is to identify and label the TPs present in the history of the stock time series through a detection technique. Once the labels are achieved, the corresponding classifier is trained to assign labels to new, invisible observations. According to the literature, improving the accuracy of the classifier leads to an increase in the forecasted TPs’ accuracy. Various intelligent classification techniques with different properties have been developed, Over the past decades, to achieve more accurate classification results. However, the parameter estimation of classifiers is done by optimizing a continuous distance-based cost function; and since the goal of the classification problem is discrete, applying a continuous cost function is unreasonable, or at least quite inefficient. To address this, we introduce a novel discrete learning-based methodology for estimating the unknown parameters of the classifier and thereby forecasting stock TPs. In contrast to the existing classification techniques, the proposed methodology is tailored to the discrete goal function of the classification. The present study chooses the multilayer perceptron (MLP), which is one of the most widely-used intelligent classification techniques, to implement the proposed methodology. Although it can be generally shown that the forecasting performance of the proposed discrete learning-based multilayer perceptron model (DIMLP) will not be worse than that of its continuous learning-based counterparts, this paper compares it to other classification techniques to determine the superiority of the proposed DIMLP model. Experimental results using randomly selected datasets from the Shanghai Stock Exchange show that the proposed DIMLP model is superior to its counterparts including classical MLP, Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Logistic Regression, Radial Basis Function Network, Generalized Logistic Regression, Probabilistic Neural Network, and AdaBoost. Consequently, further support is provided for the hypothesis that using a discrete learning-based function as a cost function for classification purposes is appropriate and effective. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
BMC Medical Informatics and Decision Making (14726947)22(1)
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. © 2022, The Author(s).
Diabetes and Metabolic Syndrome: Clinical Research and Reviews (18780334)15(6)
Background and aims: In recent decades, modeling and forecasting have played a significant role in the diagnosis and treatment of different diseases. Various forecasting models have been developed to improve data-based decision-making processes in medical systems. Although these models differ in many aspects, they all originate from the assumption that more generalizable results are achieved by more accurate models. This means that accuracy is considered as the only prominent feature to evaluate the generalizability of forecasting models. On the other side, due to the changeable medical situations and even changeable models' results, making stable and reliable performance is necessary to adopt appropriate medical decisions. Hence, reliability and stability of models' performance is another effective factor on the model's generalizability that should be taken into consideration in developing medical forecasting models. Methods: In this paper, a new reliability-based forecasting approach is developed to address this gap and achieve more consistent performance in making medical predictions. The proposed approach is implemented on the classic regression model which is a common accuracy-based statistical method in medical fields. To evaluate the effectiveness of the proposed model, it has been performed by using two medical benchmark datasets from UCI and obtained results are compared with the classic regression model. Results: Empirical results show that the proposed model has outperformed the classic regression model in terms of error criteria such as MSE and MAE. So, the presented model can be utilized as an appropriate alternative for the traditional regression model in making effective medical decisions. Conclusions: Based on the obtained results, the proposed model can be an appropriate alternative for traditional multiple linear regression for modeling in real-world applications, especially when more generalization and/or more reliability is needed. © 2021 Diabetes India
Hajiahmadi, S.,
Shayganfar, A.,
Janghorbani, M.,
Esfahani, M.M.,
Mahnam, M.,
Bakhtiarvand yousefi, N.,
Sami, R.,
Khademi, N.,
Dehghani, M. Infection and Chemotherapy (20932340)53pp. 308-318
Background: The novel coronavirus disease 2019 (COVID-19) continues to wreak havoc worldwide. This study assessed the ability of chest computed tomography (CT) severity score (CSS) to predict intensive care unit (ICU) admission and mortality in patients with COVID-19 pneumonia. Materials and Methods: A total of 192 consecutive patients with COVID-19 pneumonia aged more than 20 years and typical CT findings and reverse-transcription polymerase chain reaction positive admitted in a tertiary hospital were included. Clinical symptoms at admission and short-term outcome were obtained. A semi-quantitative scoring system was used to evaluate the parenchymal involvement. The association between CSS, disease severity, and outcomes were evaluated. Prediction of CSS was assessed with the area under the receiver-operating characteristic (ROC) curves. Results: The incidence of admission to ICU was 22.8% in men and 14.1% in women. CSS was related to ICU admission and mortality. Areas under the ROC curves were 0.764 for total CSS. Using a stepwise binary logistic regression model, gender, age, oxygen saturation, and CSS had a significant independent relationship with ICU admission and death. Patients with CSS ≥12.5 had about four-time risk of ICU admission and death (odds ratio 1.66, 95% confidence interval 1.66 - 9.25). The multivariate regression analysis showed the superiority of CSS over other clinical information and co-morbidities. Conclusion: CSS was a strong predictor of progression to ICU admission and death and there was a substantial role of non-contrast chest CT imaging in the presence of typical features for COVID-19 pneumonia as a reliable predictor of clinical severity and patient's outcome. © 2021 by The Korean Society of Infectious Diseases.
