**Author details**

L. G. Kabari1\* and E. O. Nwachukwu2

\*Address all correspondence to: ledisigiokkabari@yahoo.com

1 Department of Computer Science, Rivers State Polytechnic, Bori, Nigeria

2 Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria

## **References**


[7] Janghel, R. R., Shukla, A., Tiwari, R., & Tiwari, P. (2009). Clinical Decision support system for fetal Delivery using Ar tificial Neural Network. *2009 International Confer‐ ence on New Trends in Information and Service Science*.

**Acknowledgements**

82 Advances in Expert Systems

training and testing the system.

L. G. Kabari1\* and E. O. Nwachukwu2

\*Address all correspondence to: ledisigiokkabari@yahoo.com

1 Department of Computer Science, Rivers State Polytechnic, Bori, Nigeria

2 Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria

[1] Osheroff, J. A., Teich, J. M., & Middleton, B. F. (2006). A Roadmap for National Ac‐ tion on Clinical Decision Support. American Medical Informatics Association; 2006 June 13. Available at: http://www.amia.org/inside/initiatives/cds/. Accessed March

[2] Kohn, L. T., Corrigan, J. M., & Donaldson, M. S. (2000). To err is human: building a

[3] Miller, M., & Kearney, N. (2004). Guidelines For Clinical Practice: Development, Dis‐ semination and Implementation. *International Journal of Nursing Studies*, 41(7), 813.

[4] Field, M. J., & Lohr, K. N. (2005). Clinical Practice Guidelines: Direction for a New Program. Institute of Medicine, Committee on Clinical Practice Guidelines. Washing‐

[5] Musen, M. A. (1997). Modelling of Decision Support. *Handbook of medical informatics*, J. H. V. Bemmel and M. A. Musen, Eds. Houten: Bohn Stafleu Van Loghum.

[6] Bakpo, F. S., & Kabari, L. G. (2011). Diagnosing Skin Diseases using an Artificial Neu‐ ral Network. *Artificial Neural Networks- Methodological Advances and Biomedical Appli‐ cations, kenji suzuki (ed.)*, 978-9-53307-243-2, intech, Available from: http:// www.intechopen.com/articles/show/title/diagnosing-skin-diseases-using-an-artifi‐

safer health system. Washington, D.C.: National Academy Press.

**Author details**

**References**

20, 2009.

ton, DC. National Academy Press.

cial-neural-network.

We thank Dr. Monday Nkadam of the University of Port Harcourt Teaching Hospital for checking and correcting some of the symptoms that were used in the system. The authors also thank the staff of Linsolar and Odadiki eye clinic, Port Harcourt for using their data in


[22] Kara, S., Gu°ven, A., Okandan, M., & Dirgenali, F. Utilization of Artificial Neural Networks and Autoregressive Modeling in Diagnosing Mitral Valve Stenosis. (in press), *Computers in Biology and Medicine*.

**Chapter 4**

**Heuristics for User Interface Design in the Context of**

Several companies and institutions now realize knowledge as an active relavant for the mar‐ ket organization differentiation. This scenario explains the need for systems that assist the user in the acquisition process and knowledge management. Intelligent systems, known as expert systems (ES) [19] serve to this purpose in the extent that they have signed as facilitators in this process. These are systems that are based on expert knowledge, on any subject, in order to em‐ ulate human expertise in the specific field. To obtain this knowledge, the knowledge engi‐ neers, also called software engineers, need to develop methodologies for intelligent systems. In this area there is still no unified methodology that provides effective methods, notations and tools to aid in development. Among the most used technologies we can mention: KADS [10], MIKE [2] and Protégé [8]. KADS is a structured way of developing these systems that pro‐ vides a special focus on the characteristics and problems of development of the SE. KADS uses the waterfall model as a basis to guide the development and adds refinement stages of use and knowledge [10]. Moreover, the MIKE methodology (Model-Based and Incremental Knowl‐ edge Engineering) makes use of formal specification and semi-formal techniques during the incremental development of the system. The phases of this model are four (Figure 1), being the first, knowledge acquisition, made in a cyclic manner between the subphases of task analysis, model construction and evolution. After the acquisition phase, the design, implementation

> © 2012 Boarati et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2012 Boarati et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

distribution, and reproduction in any medium, provided the original work is properly cited.

**Cognitive Styles of Learning and Attention Deficit**

**Disorder**

Sandra Rodrigues Sarro Boarati, Cecilia Sosa Arias Peixoto and

Additional information is available at the end of the chapter

and evolution are cyclical until the system is built.

Cleberson Eugenio Forte

http://dx.doi.org/10.5772/51455

**1. Introduction**

