Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Thyroid disorder affects the regulation of various metabolic processes throughout the human body. Structural and functional disorders can affect the body and the brain. The computer-aided diagnosis system can identify the kind of thyroid disease. One such machine learning framework is presented in this paper to recognize disease existence and type. This paper presents a fuzzy adaptive feature filtration and expansion-based model to generate the most relevant and contributing features. This two-level filtration model is processed in a controlled fuzzy-based multi-measure evaluation. At the first level, the composite-fuzzy measures are combined with expert’s recommendations for identifying the ranked and relevant features. At the second level, the statistical computation-based distance measure is applied for expanding the featureset. The fuzzification is applied to the expanded featureset for transiting the continuous values to fuzzy-values. At this level, the fuzzy-based composite-measure is applied for selecting the most contributing and relevant features over the expanded dataset. This processing featureset is processed by the Extreme Learning Machine (ELM) classifier to predict the disease existence and class. Five experiments are conducted on two datasets for validating the performance and reliability of the proposed framework. The comparative analysis is conducted against the Naive Bayes, Decision Tree, Decision Forest, Random Tree, Multilevel Perceptron, and Radial Basis Function (RBF) Networks. The analysis outcome is taken in terms of accuracy, error, and relevancy-based parameters. The proposed framework claims a significant gain in accuracy, relevancy, and reduction in the error rate.