Publication: Integrating data mining techniques for naïve bayes classification: Applications to medical datasets
Issued Date
2021-09-01
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ISSN
20793197
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2-s2.0-85115322791
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Mahidol University
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SCOPUS
Bibliographic Citation
Computation. Vol.9, No.9 (2021)
Suggested Citation
Pannapa Changpetch, Apasiri Pitpeng, Sasiprapa Hiriote, Chumpol Yuangyai Integrating data mining techniques for naïve bayes classification: Applications to medical datasets. Computation. Vol.9, No.9 (2021). doi:10.3390/computation9090099 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76637
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Title
Integrating data mining techniques for naïve bayes classification: Applications to medical datasets
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Abstract
In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve bayes classifier—were combined to improve the per-formance of the latter. A classification tree was used to discretize quantitative predictors into cate-gories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas.