Analysing customer satisfaction surveys using a fuzzy rule-based decision support system: Enhancing customer relationship management

AbstractThe development of medical domain applications has been one of the most active research areas recently. One example of a medical domain application is a detection system for heart disease based on computer-aided diagnosis methods, where the data is obtained from some other sources and is evaluated by computer based applications. Up to now, computers have usually been used to build knowledge based clinical decision support systems which used the knowledge from medical experts, and transferring this knowledge into computer algorithms was done manually. This process is time consuming and really depends on the medical expert’s opinion, which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining the knowledge from the patient’s clinical data. The proposed clinical decision support system for risk prediction of heart patients consists of two phases, (1) automated approach for generation of weighted fuzzy rules and decision tree rules, and, (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity.

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Customer Relationship Management on Customer Satisfaction

International Journal of Scientific Research ◽

10.15373/22778179/apr2014/240 ◽

2012 ◽

Vol 3 (4) ◽

pp. 1-3 ◽

Cited By ~ 1

Author(s):

DR.K.VANITHA DR.K.VANITHA ◽

Keyword(s):

Customer Satisfaction ◽

Customer Relationship Management ◽

Relationship Management ◽

Customer Relationship

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

Applied Sciences ◽

10.3390/app11135810 ◽

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.

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