In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty ...In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.展开更多
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...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.展开更多
文摘In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.
文摘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.
