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Medical Diagnosis System Based on the Integrate of RS and BP Neural Network Algorithm

Author: WangJunLiang
Tutor: LuoXiaoNan
School: Sun Yat-sen University
Course: Software Engineering
Keywords: Digital Health Rough Set BP neural network Medical diagnostic Greedy algorithm
CLC: TP399-C8
Type: Master's thesis
Year: 2011
Downloads: 41
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Abstract


With the accelerated pace of the development of society and people's lives , people's health care issues has become increasingly concerned about the various high-tech has been widely applied to the medical field, has made remarkable achievements for all to see , makes digital medical become possible , and the diagnosis is the core content of the medical problems , the diagnostic accuracy and efficiency concerned about people's lives and health , medical diagnosis , digitized , it is of great significance . Rough set and neural network algorithm , by virtue of their respective advantages , has been widely applied to various fields , this paper two algorithms combining combination of medical diagnosis based on rough set and artificial neural network algorithm . Algorithm is the use of rough set a powerful attribute reduction ability , a variety of attributes to delete unnecessary redundancy part , for an important part to play a decision-making role , and then loosely coupled way , the integration of neural network algorithm , the purpose is to improve the efficiency of the system and accuracy. Rough set attribute reduction shows the powerful advantages classification ability based on the decision - making table only to retain key information , the minimum expression data reduction and obtain the knowledge , in terms of data mapping , neural the network is the most professional and has good generalization ability and fault tolerance . The strike decision table minimum reduction is an NP-hard problem . This paper proposes a greedy strategy based on the relative attributes reduct improved algorithm , the algorithm does not require the establishment of a complex matrix of difference through the the calculated attribute classification ability to identify the relative importance of attributes , and then combined in accordance with the priorities of the important properties greedy algorithm find minimum attribute reduction , improve efficiency . Artificial neural networks this paper BP algorithm , the elastic gradient descent algorithm to train the neural network , by setting the maximum error conditions as circuit training , repeatedly learning to the training ultimately achieve excellent network within the error range complies with the expectations and saved to be used to make diagnostic prediction , solve the medical field are complex nonlinear relations reach a good mapping between the diagnostic system as a subsystem part of the triple play home care health service system , a variety of heterogeneous information systems and hospital docking , set up a regional medical information shared services platform prototype .

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