Call for Paper - May 2023 Edition
IJCA solicits original research papers for the May 2023 Edition. Last date of manuscript submission is April 20, 2023. Read More

Development of an Intelligent Approach for Medical Knowledge Discovery and Decision Support

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 99 - Number 6
Year of Publication: 2014
Ali A. Sakr
Diana T. Mosa
Abdelhafiz Shehabeldien

Ali A Sakr, Diana T Mosa and Abdelhafiz Shehabeldien. Article: Development of an Intelligent Approach for Medical Knowledge Discovery and Decision Support. International Journal of Computer Applications 99(6):24-31, August 2014. Full text available. BibTeX

	author = {Ali A. Sakr and Diana T. Mosa and Abdelhafiz Shehabeldien},
	title = {Article: Development of an Intelligent Approach for Medical Knowledge Discovery and Decision Support},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {99},
	number = {6},
	pages = {24-31},
	month = {August},
	note = {Full text available}


Electronic knowledgebase (including Electronic Medical Record), together with inference procedures, form an intelligent medical information systems that offers many possibilities for health care providers. It acts as a strong base for scientific upgrading and provides an enormous support for developing new medical decisions. This paper proposes an intelligent neurosurgical decision support system framework. This framework merges the advantages of electronic medical record, rough set theory, clinical pathways, latest available scientific researches, and patient's expectations. We have designed and developed this aiming to get a support system for predicting the ideal treatment method of lumbar disc patients, in addition to evaluating the treatment plan. The system ensures future up-to-date knowledgebase, through permitting upgrading with the most recent innovations in knowledge and discoveries. This framework is expected to improve the quality of health care by providing the necessary requirements of neurosurgery domain.


  • Turban, E. 2001. Decision Support and Expert Systems. Management Support Systems. Macmillan Publishing Company. USA.
  • Alhajj, R. 2003. Extracting the Extended Entity-Relationship Model from A Legacy Relational Database. Information Systems 28, 597–618. Elsevier Science.
  • Sangjae, L. , and Ingoo, H. 2000. EDI Controls Design Support System Using Relational Database System. Decision Support Systems. vol. 29, 169–193. Elsevier.
  • Al-azmi, S. , Mohammed, A. , and Hanafi M. 2006 Patients' Satisfaction with Primary Heath Care in Kuwait after Electronic Medical Record Implementation. J Egypt Public Health Assoc. vol. 8, NO. 5& 6.
  • Tomaszewski, W. 2012. Computer-Based Medical Decision Support System Based On Guidelines, Clinical Pathways and Decision Nodes. Acta of Bioengineering and Biomechanics. vol. 14, NO. 1.
  • Ruland, C. , and Bakken, S. 2002. Developing, Implementing, and Evaluating Decision Support Systems for Shared Decision Making in Patient Care: A Conceptual Model and Case Illustration. Journal of Biomedical Informatics. vol. 35, 313-321.
  • P. Lambin, et al. 2013. Rapid Learning Health Care in Oncology – an Approach Towards Decision Support Systems Enabling Customized Radiotherapy. Radiotherapy and Oncology.
  • Hu, X. , Han, J. and Lin, T. 2004. A New Rough Sets Model Based on Database Systems, Fundamental Informatics. 1-18.
  • Munakata, T. 2008. Fundamentals of the New Artificial Intelligence Neural, Evolutionary. Fuzzy and More, 2nd ed.
  • Yao, T. , and Herbert, J. 2007 Web-Based Support Systems with Rough Set Analysis. Springer-Verlag Berlin Heidelberg. 360–370.
  • Hassanien, A. , Abraham, A. , Peters, J. , and Schaefer G. 2009. Rough Sets in Medical Informatics Applications. Applications of Soft Computing. AISC 58, 23-30. Springer- Verlag Berlin Hiedelberg.
  • Pawlak, Z. , and Skowron, A. 1994. Rough membership functions Advances in the Dempster -Shafer theory of evidence,NewYork,NY,John Wiley&Sons, 251-271.
  • Perlin, J. , Kolodner, R. , and Roswell, R. 2004. The Veterans Health Administration: Quality, Value, Accountability, and Information as Transforming Strategies for Patient Centered Care, The American Journal of Managed Care, vol. 11.
  • Lee, S. , and Abbott, P. 2003. Bayesian Networks for Knowledge Discovery in Large Datasets: Basics for Nurse Researchers. Journal of Biomedical Informatics. vol. 36, 389-399, Elsevier.
  • Kuan-Liang, K. , and Chiou-Shann F. 2011. A Rule-Based Clinical Decision Model to Support Interpretation of Multiple Data in Health Examinations. Journal of Medical Systems, vol. 35 (6), 1359-1373.
  • Riad, A. , El-Bakery, H. , and El-Ghareeb H. 2009. Mapping different software architecture paradigms to different integration techniques: highlighting driving and restraining force for each paradigm. journal of convergence information technology, vol. 4.
  • http://ntier. com
  • Connolly, T. , Begg, C. , and Holowczak, R. 2008. Business Database Systems, Addison-Wesley.
  • Inmon, W. 1996. Building the Data Warehouse, 2nd ed. , John Wiley & Sons.
  • Pinet, F. 2012. Entity-Relationship and Object-Oriented Formalisms for Modeling spatial Environmental Data. Environmental Modelling & Software, vol. 33,. 80-91, Elsevier.
  • Gibson, M. , and Arnott, D. 2005. The Evaluation of Business Intelligence: A Case Study in a Major Financial Institution. Australasian Conference on Information Systems, Sydney, Australia.
  • Kumar, A. 1998. New Techniques for Data Reduction in Database Systems for Knowledge Discovery Applications. Journal of Intelligent Information Systems, vol. 10(1), 31-48.
  • Singh, K. , Thakur, S. , and Lal, N. 2008. Vague Rough Set Techniques for Uncertainty Processing in Relational Database Model. Informatica, vol. 19, NO. 1, 113–134.
  • Gangwal, C. , and Bhaunik, R. 2012. Intuitionistic Fuzzy Rough Relational Database Model. International Journal of Database Theory and Application, vol. 5, NO. 3.