Purpose: This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods based on data, experts’ knowledge and both are considered in some cases. Besides, feature reduction and some clustering methods are used to improve their performance.Design/methodology/approach: First, the performances of classification methods are evaluated for differential diagnosis of different diseases. Then, experts' knowledge is utilized to modify the Bayesian networks' structures. Analyses of the results show that using experts' knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification. A total of ten different diseases are used for testing, taken from the Machine Learning Repository datasets of the University of California at Irvine (UCI).Findings: The proposed method improves both the computation time and accuracy of the classification methods used in this paper. Bayesian networks based on experts' knowledge achieve a maximum average accuracy of 87 percent, with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.Practical implications: The proposed methodology can be applied to perform disease differential diagnosis analysis.Originality/value: This study presents the usefulness of experts' knowledge in the diagnosis while proposing an adopted improvement method for classifications. Besides, the Bayesian network based on experts' knowledge is useful for different diseases neglected by previous papers.

Purpose: Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.Design/methodology/approach: The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.Findings: The performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.Practical implications: The proposed methodology can be applied to find the best response plan for all crises.Originality/value: In this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

Purpose Based on medical reports, it is hard to find levels of different hospitalized symptomatic COVID-19 patients according to their features in a short time. Besides, there are common and special features for COVID-19 patients at different levels based on physicians’ knowledge that make diagnosis difficult. For this purpose, a hierarchical model is proposed in this paper based on experts’ knowledge, fuzzy C-mean (FCM) clustering, and adaptive neuro-fuzzy inference system (ANFIS) classifier. Methods Experts considered a special set of features for different groups of COVID-19 patients to find their treatment plans. Accordingly, the structure of the proposed hierarchical model is designed based on experts’ knowledge. In the proposed model, we applied clustering methods to patients’ data to determine some clusters. Then, we learn classifiers for each cluster in a hierarchical model. Regarding different common and special features of patients, FCM is considered for the clustering method. Besides, ANFIS had better performances than other classification methods. Therefore, FCM and ANFIS were considered to design the proposed hierarchical model. FCM finds the membership degree of each patient’s data based on common and special features of different clusters to reinforce the ANFIS classifier. Next, ANFIS identifies the need of hospitalized symptomatic COVID-19 patients to ICU and to find whether or not they are in the end-stage (mortality target class). Two real datasets about COVID-19 patients are analyzed in this paper using the proposed model. One of these datasets had only clinical features and another dataset had both clinical and image features. Therefore, some appropriate features are extracted using some image processing and deep learning methods. Results According to the results and statistical test, the proposed model has the best performance among other utilized classifiers. Its accuracies based on clinical features of the first and second datasets are 92% and 90% to find the ICU target class. Extracted features of image data increase the accuracy by 94%. Conclusion The accuracy of this model is even better for detecting the mortality target class among different classifiers in this paper and the literature review. Besides, this model is compatible with utilized datasets about COVID-19 patients based on clinical data and both clinical and image data, as well. Highlights • A new hierarchical model is proposed using ANFIS classifiers and FCM clustering method in this paper. Its structure is designed based on experts’ knowledge and real medical process. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. • Two real datasets about COVID-19 patients are studied in this paper. One of these datasets has both clinical and image data. Therefore, appropriate features are extracted based on its image data and considered with available meaningful clinical data. Different levels of hospitalized symptomatic COVID-19 patients are considered in this paper including the need of patients to ICU and whether or not they are in end-stage. • Well-known classification methods including case-based reasoning (CBR), decision tree, convolutional neural networks (CNN), K-nearest neighbors (KNN), learning vector quantization (LVQ), multi-layer perceptron (MLP), Naive Bayes (NB), radial basis function network (RBF), support vector machine (SVM), recurrent neural networks (RNN), fuzzy type-I inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS) are designed for these datasets and their results are analyzed for different random groups of the train and test data; • According to unbalanced utilized datasets, different performances of classifiers including accuracy, sensitivity, specificity, precision, F-score, and G-mean are compared to find the best classifier. ANFIS classifiers have the best results for both datasets. • To reduce the computational time, the effects of the Principal Component Analysis (PCA) feature reduction method are studied on the performances of the proposed model and classifiers. According to the results and statistical test, the proposed hierarchical model has the best performances among other utilized classifiers. Graphical Abstract

Increasing people participation in preventive healthcare programs can improve the quality of their life and create a lot of saving. Governments can more effectively utilize their healthcare expenditures, as well. Usually, governments do not make it mandatory for people to participate in these programs but encourage them. It is necessary to design the network of preventive healthcare facilities optimally based on client choice and requirements for increasing the participation of people in such programs. Therefore, this research focuses on two models of probabilistic selection and optimal selection as different client choice for predicting and managing the preventive services networks. The limits of waiting time and the workload of each facility are also considered for ensuring the quality of care. The main characteristics of the network configuration in this paper are the number of facilities, their location, and capacity. Both models are formulated using mixed integer linear programming and a genetic algorithm is utilized in the high dimensions problems for solving them effectively and quickly. These models are implemented for actual data of Isfahan city and then their results are analyzed. The results demonstrate a significant impact of the client choice on network efficiency.

Purpose: Healthcare failure mode and effect analysis (HFMEA) identifies potential risks and defines preventive actions to reduce the effects of risks. In addition, a discrete event simulation (DES) could evaluate the effects of every improvement scenario. Consequently, a proposed integrated HFMEA-DES model is presented for quality improvement in a general hospital.Design/methodology/approach: In the proposed model, HFMEA is implemented first. As any risk in the hospital is important and that there are many departments and different related risks, all defined risk factors are evaluated using the risk priority number (RPN) for which related corrective actions are defined based on experts' knowledge. Then, a DES model is designed to determine the effects of selected actions before implementation.Findings: Results show that the proposed model not only supports different steps of HFMEA but also is highly in accordance with the determination of real priorities of the risk factors. It predicts the effects of corrective actions before implementation and helps hospital managers to improve performances.Practical implications: This research is based on a case study in a well-known general hospital in Iran.Originality/value: This study takes the advantages of an integrated HFMEA-DES model in supporting the limitation of HFMEA in a general hospital with a large number of beds and patients. The case study proves the effectiveness of the proposed approach for improving the performances of the hospital resources.