Comparative Evaluation of the Different Data Mining Techniques Used for the Medical Database

Kasperczuk, Anna; Dardzińska, Agnieszka

doi:10.1515/ama-2016-0036

Abstract

Data mining is the upcoming research area to solve various problems. Classification and finding association are two main steps in the field of data mining. In this paper, we use three classification algorithms: J48 (an open source Java implementation of C4.5 algorithm), Multilayer Perceptron - MLP (a modification of the standard linear perceptron) and Naïve Bayes (based on Bayes rule and a set of conditional independence assumptions) of the Weka interface. These classifiers have been used to choose the best algorithm based on the conditions of the voice disorders database. To find association rules over transactional medical database first we use apriori algorithm for frequent item set mining. These two initial steps of analysis will help to create the medical knowledgebase. The ultimate goal is to build a model, which can improve the way to read and interpret the existing data in medical database and future data as well.

References

1. Agrawal R., Srikant R. (1993), Fast algorithm for mining assocation rules, International Conference on Very Large Databases, 487-499.
Search in Google Scholar
2. Bouckaert R (2004), Naive Bayes Classifiers That Perform Well with Continuous Variables, Lecture Notes in Computer Science Volume, 3339, 1089-1094.10.1007/978-3-540-30549-1_106
Search in Google Scholar
3. Cheng J, Greiner R (2001), Learning Bayesian Belief Network Classifiers, Algorithms and System In Stroulia & Matwin LNAI 2056, 141-151.10.1007/3-540-45153-6_14
Search in Google Scholar
4. Dardzinska A, Romaniuk A (2015a), Incomplete distributed information systems optimization based on queries, Advances in Swarm and Computational Intelligence, Volume 9142 of LNCS Springer, 265-274.10.1007/978-3-319-20469-7_29
Search in Google Scholar
5. Dardzinska A. (2013), Action Rules Mining. Springer, pp.90.10.1007/978-3-642-35650-6
Search in Google Scholar
6. Dardzinska A., Ras Z. (2003), On Rules Discovery from Incomplete Information Systems, Proceedings of ICDM’03 Workshop on Foundations and New Directions of Data Mining, Melbourne, Florida, IEEE Computer Society.
Search in Google Scholar
7. Dardzinska A., Romaniuk A. (2015b) Queries for detailed information system selection, Position Papers of the 2015 Federated Conference on Computer Science and Information Systems, Annals of Computer Science and Information Systems vol. 6, Computer Science and Information Systems: FedCSIS, 11-15.10.15439/2015F381
Search in Google Scholar
8. Deogun J., Raghavan V., Sever H. (1994), Rough set based classification methods and extended decision tables, International Workshop on Rough Sets and Soft Computing, 302-309.
Search in Google Scholar
9. Frawley W., Piatetsky-Shapiro G., Matheus C. (1991), Knowledge discovery in databases, An overview. Knowledge Discovery in Databases, 1–27.
Search in Google Scholar
10. Freund Y, Mason L. (1999), The alternating decision tree algorithm, In Proceedings of the 16th International Conference on Machine Learning, 124-133.
Search in Google Scholar
11. Han J., Kamber M. (2006), Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, Second Edition, 21-27.
Search in Google Scholar
12. Han J., Pei J., Yin Y. (2000), Mining frequent patterns without candidate generation, ACM SIGMOD International Conference on Management of Data, 1–12.10.1145/335191.335372
Search in Google Scholar
13. Pauk J., Dardzinska A. (2012), New method for finding rules in incomplete information systems controlled by reducts in flat feet treatment, Image Proc. and Communications Challenges. Advances in Intelligent and Soft Computing, 184, 209-214.
Search in Google Scholar
14. Ras Z., Dardzinska A. (2011), From Data to Classification Rules and Action,. International Journal of Intelligent Systems, Wiley, 26(6), 572-590.10.1002/int.20485
Search in Google Scholar
15. Ras Z., Dardzinska. A., Tsay. L., Wasyluk H. (2008), Association Action Rules, IEEE International Conference on Data Mining Workshops, 283-290.10.1109/ICDMW.2008.66
Search in Google Scholar
16. Ras Z., Joshi S. (1997), Query approximate answering system for an incomplete DKBS, Fundamenta Informaticae Journal, 20(3/4), 313-324.10.3233/FI-1997-303407
Search in Google Scholar
17. Sliwinska-Kowalska M., Niebudek-Bogusz E., Fiszer M., et al. (2006), The prevaence and risk factors for occupational voice disorders in teachers, Folia Phoniatr. Logop, 58(2), 85-101.
Search in Google Scholar
18. Thair Nu Phyu (2009), Survey of Classification Techniques in Data Mining, Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol I IMECS.
Search in Google Scholar
19. Yoo I, Alafaireet P, Marinov M, et al. (2012), Data mining in healthcare and biomedicine, A survey of the literature. Journal of medical systems, 36(4), 2431-2448.10.1007/s10916-011-9710-521537851
Search in Google Scholar

Comparative Evaluation of the Different Data Mining Techniques Used for the Medical Database

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