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Artificial Bee Colony-Based Associative Classifier for Healthcare Data Diagnosis
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Author(s): M. Nandhini (Department of Computer Science, Government Arts College, Udumalpet, India), S. N. Sivanandam (Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, India)and S. Renugadevi (Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, India)
Copyright: 2021
Pages: 17
Source title:
Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning
Source Author(s)/Editor(s): Geeta Rani (Manipal University Jaipur, India)and Pradeep Kumar Tiwari (Manipal University Jaipur, India)
DOI: 10.4018/978-1-7998-2742-9.ch012
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Abstract
Data mining is likely to explore hidden patterns from the huge quantity of data and provides a way of analyzing and categorizing the data. Associative classification (AC) is an integration of two data mining tasks, association rule mining, and classification which is used to classify the unknown data. Though association rule mining techniques are successfully utilized to construct classifiers, it lacks in generating a small set of significant class association rules (CARs) to build an accurate associative classifier. In this work, an attempt is made to generate significant CARs using Artificial Bee Colony (ABC) algorithm, an optimization technique to construct an efficient associative classifier. Associative classifier, thus built using ABC discovered CARs achieve high prognostic accurateness and interestingness value. Promising results were provided by the ABC based AC when experiments were conducted using health care datasets from the UCI machine learning repository.
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