J Med Syst - A computer aided diagnosis system for thyroid disease using extreme learning machine.

Tópicos

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Resumo

In this paper, we present an effective and efficient computer aided diagnosis (CAD) system based on principle component analysis (PCA) and extreme learning machine (ELM) to assist the task of thyroid disease diagnosis. The CAD system is comprised of three stages. Focusing on dimension reduction, the first stage applies PCA to construct the most discriminative new feature set. After then, the system switches to the second stage whose target is model construction. ELM classifier is explored to train an optimal predictive model whose parameters are optimized. As we known, the number of hidden neurons has an important role in the performance of ELM, so we propose an experimental method to hunt for the optimal value. Finally, the obtained optimal ELM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative new feature set and the optimal parameters. The effectiveness of the resultant CAD system (PCA-ELM) has been rigorously estimated on a thyroid disease dataset which is taken from UCI machine learning repository. We compare it with other related methods in terms of their classification accuracy. Experimental results demonstrate that PCA-ELM outperforms other ones reported so far by 10-fold cross-validation method, with the mean accuracy of 97.73% and with the maximum accuracy of 98.1%. Besides, PCA-ELM performs much faster than support vector machines (SVM) based CAD system. Consequently, the proposed method PCA-ELM can be considered as a new powerful tools for diagnosing thyroid disease with excellent performance and less time.

Resumo Limpo

paper present effect effici comput aid diagnosi cad system base principl compon analysi pca extrem learn machin elm assist task thyroid diseas diagnosi cad system compris three stage focus dimens reduct first stage appli pca construct discrimin new featur set system switch second stage whose target model construct elm classifi explor train optim predict model whose paramet optim known number hidden neuron import role perform elm propos experiment method hunt optim valu final obtain optim elm model proceed perform thyroid diseas diagnosi task use discrimin new featur set optim paramet effect result cad system pcaelm rigor estim thyroid diseas dataset taken uci machin learn repositori compar relat method term classif accuraci experiment result demonstr pcaelm outperform one report far fold crossvalid method mean accuraci maximum accuraci besid pcaelm perform much faster support vector machin svm base cad system consequ propos method pcaelm can consid new power tool diagnos thyroid diseas excel perform less time

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