Abstract
Medical diagnosis and extracting patterns that can be converted into useful knowledge is a challenging work for researchers. Medical records are based on real-time data that have high dimensionality which makes task of extracting patterns even more complex. Prediction of various diseases often suffers due to large dimensionality that includes thyroid, diabetes, cancer, etc. Data mining is a process of finding out information and constructing a knowledge base from a huge amount of data, which remains useful. Data mining achieves these tasks involving classification, clustering, regression, and prediction. Reduction of dimensionality with the motive of knowledge discovery is an essential aspect of data mining as it helps in prediction/determination for various data analytics filed like medical diagnosis, business modeling, and government data analysis. This paper presents cooperative method for classification of thyroid using machine learning fusing C4.5 and random forest classification technique (CCTML). Experimental results are compared with other conventional techniques and the results obtained through CCTML reported better classification accuracy and outperform conventional methods.
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Dharamkar, B., Saurabh, P., Prasad, R., Mewada, P. (2020). An Ensemble Approach for Classification of Thyroid Using Machine Learning. In: Das, H., Pattnaik, P., Rautaray, S., Li, KC. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 1119. Springer, Singapore. https://doi.org/10.1007/978-981-15-2414-1_2
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DOI: https://doi.org/10.1007/978-981-15-2414-1_2
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