On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
ESTDD: Expert system for thyroid diseases diagnosis
Expert Systems with Applications: An International Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A comparative study on thyroid disease diagnosis using neural networks
Expert Systems with Applications: An International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Multi-category bioinformatics dataset classification using extreme learning machine
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Patient Outcome Prediction with Heart Rate Variability and Vital Signs
Journal of Signal Processing Systems
A Three-Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis
Journal of Medical Systems
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Learning capability and storage capacity of two-hidden-layer feedforward networks
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
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.