J Med Syst - A three-stage expert system based on support vector machines for thyroid disease diagnosis.

Tópicos

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Resumo

In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). Finally, the obtained optimal SVM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed expert system (FS-PSO-SVM) has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. The proposed system has been compared with two other related methods including the SVM based on the Grid search technique (Grid-SVM) and the SVM based on Grid search and principle component analysis (PCA-Grid-SVM) in terms of their classification accuracy. Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.

Resumo Limpo

paper present threestag expert system base hybrid support vector machin svm approach diagnos thyroid diseas focus featur select first stage aim construct divers featur subset differ discrimin capabl switch featur select model construct second stage obtain featur subset fed design svm classifi train optim predictor model whose paramet optim particl swarm optim pso final obtain optim svm model proceed perform thyroid diseas diagnosi task use discrimin featur subset optim paramet effect propos expert system fspsosvm rigor evalu thyroid diseas dataset common use among research use machin learn method thyroid diseas diagnosi propos system compar two relat method includ svm base grid search techniqu gridsvm svm base grid search principl compon analysi pcagridsvm term classif accuraci experiment result demonstr fspsosvm signific outperform one addit compar exist method previous studi propos system achiev highest classif accuraci report far fold crossvalid cv method mean accuraci maximum accuraci promis propos fspsosvm expert system might serv new candid power tool diagnos thyroid diseas excel perform

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