Artif Intell Med - Case-based reasoning support for liver disease diagnosis.

Tópicos

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Resumo

JECTIVES: In Taiwan, as well as in the other countries around the world, liver disease has reigned over the list of leading causes of mortality, and its resistance to early detection renders the disease even more threatening. It is therefore crucial to develop an auxiliary system for diagnosing liver disease so as to enhance the efficiency of medical diagnosis and to expedite the delivery of proper medical treatment.METHODS: The study accordingly integrated the case-based reasoning (CBR) model into several common classification methods of data mining techniques, including back-propagation neural network (BPN), classification and regression tree, logistic regression, and discriminatory analysis, in an attempt to develop a more efficient model for early diagnosis of liver disease and to enhance classification accuracy. To minimize possible bias, this study used a ten-fold cross-validation to select a best model for more precise diagnosis results and to reduce problems caused by false diagnosis.RESULTS: Through a comparison of five single models, BPN and CBR emerged to be the top two methods in terms of overall performance. For enhancing diagnosis performance, CBR was integrated with other methods, and the results indicated that the accuracy and sensitivity of each CBR-added hybrid model were higher than those of each single model. Of all the CBR-added hybrid models, the BPN-CBR method took the lead in terms of diagnosis capacity with an accuracy rate of 95%, a sensitivity of 98%, and a specificity of 94%.CONCLUSIONS: After comparing the five single and hybrid models, the study found BPN-CBR the best model capable of helping physicians to determine the existence of liver disease, achieve an accurate diagnosis, diminish the possibility of a false diagnosis being given to sick people, and avoid the delay of clinical treatment.

Resumo Limpo

jectiv taiwan well countri around world liver diseas reign list lead caus mortal resist earli detect render diseas even threaten therefor crucial develop auxiliari system diagnos liver diseas enhanc effici medic diagnosi expedit deliveri proper medic treatmentmethod studi accord integr casebas reason cbr model sever common classif method data mine techniqu includ backpropag neural network bpn classif regress tree logist regress discriminatori analysi attempt develop effici model earli diagnosi liver diseas enhanc classif accuraci minim possibl bias studi use tenfold crossvalid select best model precis diagnosi result reduc problem caus fals diagnosisresult comparison five singl model bpn cbr emerg top two method term overal perform enhanc diagnosi perform cbr integr method result indic accuraci sensit cbrad hybrid model higher singl model cbrad hybrid model bpncbr method took lead term diagnosi capac accuraci rate sensit specif conclus compar five singl hybrid model studi found bpncbr best model capabl help physician determin exist liver diseas achiev accur diagnosi diminish possibl fals diagnosi given sick peopl avoid delay clinic treatment

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