Rule-Based Post Solution Analysis of Decision Support Problems: Some Preliminary Results

1986 ◽  
pp. 301-315 ◽  
Author(s):  
Farrokh Mistree ◽  
H. M. Karandikar ◽  
Saiyid Kamal
Keyword(s):  
Decision Support ◽  
Rule Based ◽  
Preliminary Results

scholarly journals An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty

2021 ◽  
Vol 11 (13) ◽  
pp. 5810
Author(s):  
Faisal Ahmed ◽  
Mohammad Shahadat Hossain ◽  
Raihan Ul Islam ◽  
Karl Andersson
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Experimental Result ◽  
Rule Base ◽  
Rule Based ◽  
Belief Rule Base

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.

scholarly journals Penentuan Prioritas Pengambilan Pesanan Barang Oleh Angkutan Kota dengan Metode Rule-Based System

2018 ◽  
Vol 8 (2) ◽  
pp. 81
Author(s):  
Nur Aini Rakhmawati ◽  
Aditya Septa Budi ◽  
Faizal Johan Altetiko ◽  
Fajar Ramadhani ◽  
Nanda Kurnia Wardati ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Public Transportation ◽  
Storage Capacity ◽  
Urban Transportation ◽  
Urban Transport ◽  
Rule Based ◽  
Rule Based System ◽  
Passenger Transportation

Angkotin is a system that provides an alternative for urban transport to not only be used for passenger transportation, but also as freight service. Therefore, it needs a decision support system for taking order to delivery to the destination according to each criterion from urban transportation. The method used to develop this decision support system is a rule-based system. The result of this research is a decision support system that can help public transportation to find orders that can be taken based on four factors, such as distance, direction, route code, and status of storage capacity. Based on these four factors, the system can provide an order recommendation under the appropriate conditions through the Angkotin application. Based on our experiment, our system performs on 7 seven cases as expected.   

Fuzzy rule-based decision support system for evaluation of long-established forest restoration projects

2016 ◽  
Vol 24 (3) ◽  
pp. 298-305 ◽  
Author(s):  
Anahí Ocampo-Melgar ◽  
Aida Valls ◽  
Jose Antonio Alloza ◽  
Susana Bautista
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Forest Restoration ◽  
Fuzzy Rule ◽  
Rule Based

Modeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support

2012 ◽  
pp. 255-272 ◽  
Author(s):  
Jose M. Alonso ◽  
Ciro Castiello ◽  
Marco Lucarelli ◽  
Corrado Mencar
Keyword(s):  
Decision Support ◽  
Fuzzy Rule ◽  
Linguistic Knowledge ◽  
Medical Decision ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Medical Decision Support ◽  
Modeling Methodology ◽  
Stage Renal Disease ◽  
End Stage

Decision support systems in Medicine must be easily comprehensible, both for physicians and patients. In this chapter, the authors describe how the fuzzy modeling methodology called HILK (Highly Interpretable Linguistic Knowledge) can be applied for building highly interpretable fuzzy rule-based classifiers (FRBCs) able to provide medical decision support. As a proof of concept, they describe the case study of a real-world scenario concerning the development of an interpretable FRBC that can be used to predict the evolution of the end-stage renal disease (ESRD) in subjects affected by Immunoglobin A Nephropathy (IgAN). The designed classifier provides users with a number of rules which are easy to read and understand. The rules classify the prognosis of ESRD evolution in IgAN-affected subjects by distinguishing three classes (short, medium, long). Experimental results show that the fuzzy classifier is capable of satisfactory accuracy results – in comparison with Multi-Layer Perceptron (MLP) neural networks – and high interpretability of the knowledge base.

Rule-Based Ontology Decision Model

2016 ◽  
pp. 271-291
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Rule Based ◽  
The Matrix ◽  
Markov Decision ◽  
Minimization Technique ◽  
Complex Decision

In chapter 7, we examined some selected case study applications of some decision support systems. Those considered were the matrix-based used in determining labour cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique and Markov decision process. As earlier discussed, our focus will be on rule-based decision support systems. This is because rule-based systems are more encompassing and can easily be employed to deal with complex decision about construction activities. Hence in this chapter, an overview of rule-based decision system will be examined.

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