A computer aided diagnosis system for thyroid disease using extreme learning machine.
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.
College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.
Li-Na
LN
Li
Ji-Hong
JH
Ouyang
Hui-Ling
HL
Chen
Da-You
DY
Liu
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D013959:Thyroid Diseases / Q000175:diagnosis*