Features Reduction In Case Retrieval For Diabetes Dataset.

Features Reduction In Case Retrieval For Diabetes Dataset.

In reality, the organizations often have the great quantity of data stored in the databases. The large size of data in terms of the number of attributes and objects make the analysis process becomes very difficult as the data are complex. In order to overcome this problem, the use of sufficient numb...

Full description

Saved in:
Bibliographic Details
Main Author: Bala, Abdalla Ali Abdalla
Format: Thesis
Language:eng
eng
Published: 2007
Subjects:
Q Science (General)
Online Access:https://etd.uum.edu.my/58/1/abdalla_ali.pdf
https://etd.uum.edu.my/58/2/abdalla_ali.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-uum-etd.58
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
topic Q Science (General)
spellingShingle Q Science (General)
Bala, Abdalla Ali Abdalla
Features Reduction In Case Retrieval For Diabetes Dataset.
description In reality, the organizations often have the great quantity of data stored in the databases. The large size of data in terms of the number of attributes and objects make the analysis process becomes very difficult as the data are complex. In order to overcome this problem, the use of sufficient number of attributes and objects will contribute to get the best solution. There are many techniques which can be employed to reduce the number of attributes in the dataset. In this study, two techniques core using, namely rough set theory and Case-Based Reasoning were applied to the medical dataset.
format Thesis
qualification_name masters
qualification_level Master's degree
author Bala, Abdalla Ali Abdalla
author_facet Bala, Abdalla Ali Abdalla
author_sort Bala, Abdalla Ali Abdalla
title Features Reduction In Case Retrieval For Diabetes Dataset.
title_short Features Reduction In Case Retrieval For Diabetes Dataset.
title_full Features Reduction In Case Retrieval For Diabetes Dataset.
title_fullStr Features Reduction In Case Retrieval For Diabetes Dataset.
title_full_unstemmed Features Reduction In Case Retrieval For Diabetes Dataset.
title_sort features reduction in case retrieval for diabetes dataset.
granting_institution Universiti Utara Malaysia
granting_department College of Arts and Sciences (CAS)
publishDate 2007
url https://etd.uum.edu.my/58/1/abdalla_ali.pdf
https://etd.uum.edu.my/58/2/abdalla_ali.pdf
_version_ 1747826834055102464
spelling my-uum-etd.582013-07-24T12:05:27Z Features Reduction In Case Retrieval For Diabetes Dataset. 2007-08 Bala, Abdalla Ali Abdalla College of Arts and Sciences (CAS) Faculty of Information Technology Q Science (General) In reality, the organizations often have the great quantity of data stored in the databases. The large size of data in terms of the number of attributes and objects make the analysis process becomes very difficult as the data are complex. In order to overcome this problem, the use of sufficient number of attributes and objects will contribute to get the best solution. There are many techniques which can be employed to reduce the number of attributes in the dataset. In this study, two techniques core using, namely rough set theory and Case-Based Reasoning were applied to the medical dataset. 2007-08 Thesis https://etd.uum.edu.my/58/ https://etd.uum.edu.my/58/1/abdalla_ali.pdf application/pdf eng validuser https://etd.uum.edu.my/58/2/abdalla_ali.pdf application/pdf eng public masters masters Universiti Utara Malaysia Althoff, K. D., Bergmann, R., Wess, S., Manago, M., Auriol, E., Larichev, 0. I., Bolotov, A., Zhuravlev, Y. I., & Gurov, S. I. (1998). Case-Based Reasoning for Medical Decision Support Tasks: The INRECA Approach. Journal of Artificial Intelligence In Medical, 12(1), 25-41 . Aamodt, A. (1993). Explanation-driven retrieval, ruse and learning of cases. University of Kaiserslautem SEKI Report S-93- 12 (SFB 314) 279-284. Aamodt, A, & Plaza, E. (1994) Case Based Reasoning: Foundational issues, Methodological Variations and system Approaches. A1 Communications. IOS press, 7 (I), pp.39-59. Bemer, E. S., Webster, G. D., Shugennan, A. A., Jackson, J. R., Algina, J., Baker, A. L.,Ball, E. V., Cobbs, C. G., Dennis, V. W., Frenkel, E. P., Hudson, L. D., Mancall, E. L., Rackley, C. E., Taunton, 0. D. (1994). Performance of four computer based diagnostic systems. Berkovsky, S. Y., & Ben-Asher, Y. (2004). UNSO: Unspecified Onntologies for Peer-to-peer Ecommerce Applications. In Proc. Of the International Conference on Informatics, Turkey. Buckles, B., & Petry, F. (1982). A fuzzy model for relational databases. Int J. Fuzzy Sets Syst., Vol. 7, pp. 213-226. Balaa, Z. E., Strauss, A., Uziel, P., Maximini, K., & Traphoner, R. (2003). FM-Ultranet: A Decision Support System using Case-based Reasoning, Applied to Ultrasonography. Proceedings of the International Colference on Case-Based Reasoning, 37-44. Campin, J., Paton, N., & Williams, M. (1997). Specifying active database systems in an object- oriented framework. Softw. Eng. Knowl. Eng. 7(1), 101-123). Ceri, S. & Fraternali, P. (1997). Designing Applications with Objects and Rules: The IDEA Methodology. International Series on Database Systems and Applications, Addison- Wesley Longman, Reading, MA. Chakravarthy, S. (1989). Rule management and evaluation: An active DBMS perspective. Sigmoid Rec. 18, 3, pp. 20-28. Diaz, 0. (1992). Deriving rules for constraint maintenance in an object-oriented database. In Proceedings of the International Conference on Databases and Expert Systems DEXA, I. R. A. M. Tjoa, Ed., Springer-Verlag, pp. 332-337. Diagnostic Strategies (1999). Expert System Development Series Introduction to Case-Based Reasoning, Retrieved, 2007, from http://www. Diagnostic Strategies.com Differences between type 1 and type 2 diabetes, (2006) Juvenile Diabetes Research Foundation. Retrieved June 18, 2007, from http://www.jdrf.org.au/publications/factsheets/differences-between_type_1_and_type_2.pdf Eddy, D. M. (1990). The challenge. Journal of American Medical Association, 263, pp. 287-290. Fernandez, I. B. & Aha, D. W. (1996). Case-Based problem solving for Knowledge Management system. In Proceeding of the 12th Annual International Floroda Artificial Intelligence Research Symposium (FLAIRS): Knowledge Munagement Track. NCARAI Technical report AIC-99-005. Gatziu, S., & Dittrich, K. (1994). Events in an active object-oriented database. In Proceedings of the First International Workshop on Rules in Database Systems, N. Paton and M. Williams, Eds., Springer-Verlag, pp. 23-39. George, R., Srikanth, R., Petry, F. E., & Buckles, B. P. (1996). Uncertainty management issues in object-oriented database systems, IEEE Trans. Fuzzy Sys, vol. 4,pp.179-192. Hanson, E. N., & Widorn, J. (1992). An overview of production rules in database systems. Tech. Rep., University of Florida, Department Computer and Information. Hugo, C. H., & Tania, C. D., (2003). Analyzing the use of Dynamic Weights in Legal Case Based System. Edinburgh, Scotland, UK. Hejlesen, Plogmann, S., & Cavan, D. (2000). DiasNet - An Internet Tool for Communication and Education in Diabetes. International Symposium on Computer and Diabetes Care. Rochester MN, September 8-10. Kolodner, J. (1993). Case-Based Reasoning. San Mateo, CA: Morgan Kaufman Publishers. Jaulent, M. C., Bozec, C. L., Zapletal, E., & Degoulet, P. (1997). A Case-based Reasoning Method for Computer-Assisted Diagnosis in Hisopathology. Journals of Artificial Intelligence in Medicine, 239-242. Lin, T. Y., & Cercone, N. (1997). Rough Sets and Data Mining- Analysis of Imperfect Data, Kluwer Academic Publishers, Boston, London, Dordrecht, pp. 430. Leake, D. B. (1996). Case-Based Reasoning: Experience, Lessons and future Direction. Menlo Park: AAAI Press. Limthanmaphon, B., & Zhang, Z. (2002). Web Service Composition with Case-Based Reasoning. Department of Mathematic and Computing, University of Southern Quessnsland, Toowoomba, Australia. Lopez, R. & Plaza, E. (1997). Case-based Reasoning: An Overview. A1 Communication Journal, 10(1), pp 21-29. Liu, J. N. K., & Sin, D. K. Y. (1999). Evaluating case-based reasoning and evolution strategies for machine maintenance. Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on. IEEE (pp.480 - 485 vo1.2). (IEEE Document Reproduction Service No. 10.1109/ICSMC. 1999.825308). Li, K. & Liu, Y. (2002). Rough set based attribute reduction approach in data mining. Machine Learning and Cybernetics, 2002 Proceedings. 2002 International Conference on. Volume 1,4-5 Nov. 2002 Page(s):60-63vol.1. Digital Object Identifier 10.1 109/ICMLC.2002.1176709. Medical computing, Shortliffe EH. The science of biomedical computing. Med Inform 1984; 9: 185-93 Retrieved June 10, 2007, from http://www.openclinical.org/healthinformatics.html Montani, S., Portinale, L., Leonardi, G., & Bellazi, R. (2003). Applying Case-based Retrieval to Hemodialysis Treatment. Proceedings of the International Conference on Case-Based Reasoning, 53-62. Miller, R. A. (1994). Medical Diagnosis Decision Support Systems-Past, Present, and Future. 1, 8-27. Retrieved June 20, 2007, from http://www.cpmc.columbia.edu/Nillson, M., Funk, P., & Sollenborn, M. (2003). Complex Measurement Classification in Medical Applications Using a Case-Based Approach. Proceedings of rhe International Conference on Cuse-Bused Reasoning, 63-72. Oehrn, A. (1993). Rough logic control. In: (Project), Technical Report. Knowledge Systems Group, The Norwegian University of Science and Technology, Trondheim, Norway. Pal, S. K., & Skowron, A. Fuzzy Sets, Rough Sets and Decision Making Processes. Springer-Verlag, Singapore (in preparation) Pawlak, Z. (1992). Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Norwell, MA, USA. Pawlak, Z., Grzyrnala-Busse, J., Slowinski, R., & Ziarko, W. (1995). Rough sets. Commun. of ACM, 38, pp. 88-95. Polkowski, L., & Skowron, A. (1998). Rough Sets in Knowledge Discovery, Physica-Verlag, l(2). Perner, P., Gunther, T., & Pemer, H. (2003). Airborne Fungi Identification by Case-based Reasoning. Proceedings of the International Conference on Case-Based Reasoning, 73-79. Qiufen Qi Dalhousie University. Case-Based Reasoning (CBR) Process in Diagnosis, Retrieved June 13, 2007, from http://web.his.uvic.ca/rle/2004/00i.ppt? Riesbeack, C. K., & Schank, R. C. (1989). Inside CBR. Hillside, New Jersey, USA: Lawrence Erlbaum Associates. Slowinski, R. (1992). Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory. Kluwer Academic Publishers, Boston, London, Dordrecht. Stonebraker, M., & Kemnitz, G. (1991). The Progress next-generation database management system. Commun. ACM 34, Oct., pp. 78-92. Smyth, B., & Keane, M., (1995). Experimental on Adaptation-Guided Retrieval in Case based Design. In Topics in Case-Based Reasoning Proceedings of the International Conference on Case-Based Reasoning, iCCBR95. LNAi series, Springer, Sesimbra, Portugal. Tsumoto, S., Kobayashi, S., Yokomori, T., Tanaka, H., & Nakamura, A. (1996). The Fourth Internal Workshop on Rough Sets, Fuzzy Sets and Machine Discovery. The University of Tokyo. Vaishnavi & Kuechler (2004). Design Research in information system. Retrieved June 15,2007, from http://www.isworld.org/Researchdesign/drisISworld.htm Widom, J. (1992). A denotational semantics forthe Starburst production rule language. Sigmod Rec. 2 1,3,4-9. Ziarko, W. (1993). Rough Sets, Fuzzy Sets and Knowledge Discovery. Proceedings of the International Workshop on Rough Sets and Knowledge Discovery (RSKD'93), Banff, Alberta, Canada .October 12-15, Springer-Verlag, Berlin.

Similar Items