scholarly journals Development of Prediction Models for the Dengue Survivability Prediction: An Integration of Data Mining and Decision Support System

2019 ◽  
Vol 8 (10) ◽  
pp. 2199-2205
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Prediction Models ◽  
Viral Disease ◽  
Rule Based ◽  
Medical Practitioners ◽  
Tree Models

Dengue is a viral disease that has been feared by people globally. Due to its rapid prevalence and increasing threat, this study explored on the use of data mining techniques together with decision support system to develop prediction models of dengue survivability. This study was focused on three important points namely: identify significant predictor attributes to dengue survivability prediction, development of a rule-based and decision tree models for dengue survivability prediction, and the development of a dengue survivability platform for prediction purposes. The developed rule-based and decision tree models were compared according to accuracy and they underwent the 10-fold cross validation procedure and were integrated in the system to provide a platform to predict the survivability of a patient given the input medical data using a client-server configuration via the Internet. The result of the prediction for the dengue survivability may be used as an intervention by medical practitioners in the general management of dengue cases.

scholarly journals Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm

2019 ◽  
Vol 5 (1) ◽  
pp. 57
Author(s):  
Cut Fiarni ◽  
Evasaria M. Sipayung ◽  
Prischilia B.T. Tumundo
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Educational Data Mining ◽  
Educational Process ◽  
Student Interest ◽  
The Impact

Background: Educational data mining is an emerging trend, especially in today Big Data Era. Numerous method and technique already been implemented in order  to improve its process to gain better understanding of the educational process and to extract knowledge from various related data, but the implementation of these methods into Decision support system (DSS) application still limited, especially regarding help to choose university sub majors .Objective: To design an academic decision support system (DSS) by adopting Theory of Reasoned Action (TRA) concept and using Data Mining as a factor analytic apporach to extract rules for its knowledge model.Methods: We implemented factor analysis method and decision tree method  of C.45 to produce rules of the impact course of the sub- majors and the job interest as the basic rules of the DSS.Results: The proposed academic decision support system able to give sub majors recommendations in accordance with student interest and competence, with 79.03% of precision and 61.11% of recall. Moreover, the system also has a dashboard feature that shows the information about the statistic of students in each sub majors.Conclusion: C.45 algorithm and factor analysis are suitable to build a knowledge model for Academic Decision Support System for Choosing Information System Sub Majors Bachelor Programs. This system could also help the academic adviser on monitoring and make decision accordance with that academic information

Research of data mining and web technology in university discipline construction decision support system based on MVC model

2019 ◽  
Vol 38 (3) ◽  
pp. 610-624
Author(s):  
Zhaokun Huang ◽  
Yufang Liang
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Colleges And Universities ◽  
Bayesian Theory ◽  
Data Mining Algorithm ◽  
Content Type ◽  
Mvc Model

Purpose Taking the discipline construction in colleges and universities as the application background, based on the research on data mining technology and decision support system technology, the data generated by university management information system are effectively utilized. The paper aims to discuss these issues. Design/methodology/approach Based on the Beijing Key Discipline Information Platform as the data source, the decision tree algorithm of data mining is studied. On the basis of decision tree C4.5, the Bayesian theory is applied to the post-pruning operation of the decision tree. Findings A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with browser/server (B/S) structure is completed and implemented. Originality/value A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with B/S structure is completed and implemented.

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.   

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