A Hybrid Predicting Stock Return Model Based on Bayesian Network and Decision Tree

2014 ◽  
pp. 218-227
Author(s):  
Shou-Hsiung Cheng
Keyword(s):  
Decision Tree ◽  
Bayesian Network ◽  
Stock Return ◽  
Model Based

Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

Cost-effectiveness of rabies post-exposure prophylaxis in the context of very low rabies risk: A decision-tree model based on the experience of France

Vaccine ◽  
2015 ◽  
Vol 33 (20) ◽  
pp. 2367-2378 ◽  
Author(s):  
Florence Ribadeau Dumas ◽  
Dieynaba S. N’Diaye ◽  
Juliette Paireau ◽  
Philippe Gautret ◽  
Hervé Bourhy ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Post Exposure Prophylaxis ◽  
Tree Model ◽  
Post Exposure ◽  
Model Based ◽  
Exposure Prophylaxis

scholarly journals Bayesian Network Models with Decision Tree Analysis for Management of Childhood Malaria in Malawi

2020 ◽  
Author(s):  
Sanya B. Taneja ◽  
Gerald P. Douglas ◽  
Gregory F. Cooper ◽  
Marian G. Michaels ◽  
Marek J. Druzdzel ◽  
...  
Keyword(s):  
Decision Tree ◽  
Bayesian Network ◽  
Clinical Features ◽  
Predictive Models ◽  
Clinical Decision Making ◽  
Performance Metrics ◽  
Expert Knowledge ◽  
Clinical Decision ◽  
Automated Method ◽  
Childhood Malaria

Abstract Background: Malaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare worker in judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT).Methods: We developed two BN models from data that were collected in a national survey of outpatient encounters of children in Malawi. The target diagnosis is taken as the result of mRDT. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method followed by modifications guided by expert knowledge. The performance of the BN models was compared to other statistical models on a range of performance metrics. We developed a decision tree that integrates predictions from these predictive models with the costs of mRDT and a course of recommended treatment. Results: Compared to the logistic regression and random forest models, the BN models had similar accuracy of 64% but had higher sensitivity at the cost of lower specificity at the default threshold. Sensitivity analysis of the decision tree showed that at low (below 0.04) and high (above 0.4) probabilities of malaria in a child, the preferred decision that minimizes expected costs is not to perform mRDT.Conclusion: In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support such clinical decision making.

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