The week's pick
Reseach Article
Case based Reasoning for Treatment and Management of Diabetes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 145 - Number 4 |
| Year of Publication: 2016 |
| Authors: Mark K. Kiragu, Peter W. Waiganjo |
10.5120/ijca2016910552
|
Mark K. Kiragu, Peter W. Waiganjo . Case based Reasoning for Treatment and Management of Diabetes. International Journal of Computer Applications. 145, 4 ( Jul 2016), 20-29. DOI=10.5120/ijca2016910552
Abstract
This research focused on the use of case based reasoning (CBR) for treatment and management of diabetes. CBR is a field of artificial intelligence where one uses past cases as resolution for similar problems. The concept is based on dynamic memory theory where human beings solve problems by recalling encountered cases [1]. This research has applied CBR in the field of medicine for treatment and management of diabetes. Diabetes is a family of metabolic disease condition where the patient has elevated blood glucose. There is a rise on the prevalence of diabetes in Kenya with over 2 Million Kenyans suffering from the condition [2]. Damage to nerves, heart failure, kidney failure blindness and amputations are among the diabetes associated complications. Some of key challenges encountered during the management of diabetes include lack of insulin, high cost of drugs, an overworked workforce and low awareness among others. A formative questionnaire was conducted to find out the viability of previous experience in problem resolution and later a summative questionnaire administered to medical experts to measure the outcome of the research. A prototype was developed using JCOLIBRI framework and trained with a total of 60 cases. 40 cases were type 1 and the remaining 20 cases type 2. A test data of 20 cases was used to measure the accuracy of the system. The key variables used in test were blood glucose, HBA1C (average blood glucose over 3 months), weight and height. The diagnosis predicted by the system was compared against the one obtained by the expert and the results were as follows. When tested with the 3 parameters (Blood Glucose, Height & Weight) the system had a mean accuracy of 28% before revision (3rd Cycle of CBR) and after the first revision (3rd Cycle of CBR) the system attained a mean accuracy of 70% with the 3 parameters. When tested with 1 parameter (Blood Sugar) after revision (3rd Cycle of CBR) the system returned a mean accuracy of 90% .The accuracy was based on the difference of solution applied between an expert judgment and the system judgment. The level of blood glucose is the key factor to consider during diabetes diagnosis. The research concluded that CBR is more accurate after the revision cycle and as the number of cases increase.
References
- Aamodt, A.,Plaza, E. (1994).Case-Based Reasoning:Foundational Issues,Methodological Variations, and System Approaches
- Medical Journal of Therapeutics(2008) Obstacle to Diabetes Care in Kenya.
- Wei-Fan, C., and Kuo-Chuan, Y.(2006). Creating a Case-Based Reasoning Digital Library to Improve Learning in an Introductory Programming Course.
- Craig, S. and David, W.(2003).Toward memory-based reasoning.
- Watson,I. (1999). Case-based resoning is a methodology not a technology
- Adebayo, K.,Adekoya, A. and Ekwonna, C. (2014).Temperament and Mood Detection Using Case-Based Reasoning.
- Antti, V., Olli, A. and Eero, H. Combining Case-Based Reasoning and Semantic Indexing in a Question-Answer Service.
- Ashok, K. and Craw2, S. Design, innovation and case-based reasoning.
- Breese, J.(1994). Decision-Theoretic Case-Based Reasoning.
- Mariana, M. and Ernesto, O.(2010).Integration of Rule Based Expert Systems and Case Based Reasoning in an Acute Bacterial Meningitis Clinical Decision Support System.
- Shahina, B. and Peter, F. (2009).Case-based systems in health sciences - a case study in the field of stress management.
- Sarah,J. (2006).Using Case-Based Reasoning for Spam Filtering.
- Salha, B. Abdullah(1997) The fundamentals of case-based reasoning: application to a building defect problem.
- Surjeet, D. and Dr. Vijay.(2011).Case Retrieval Optimization of Case-based reasoning through Knowledge-Intensive Similarity Measures.
- Ting-Peng, L. Analogical reasoning and case-based learning in model management systems.
- Zouhair, A. ,Bertelle, C. (2012). Dynamic Case-Based Reasoning Based on the Multi-Agent Systems: Individualized Follow-Up of Learners in Distance Learning.
- Sima, S. (2013).Case-Based Reasoning for Diagnosis and Solution Planning.
- A-Xing, Z., James, E. Burt . A Case-based Reasoning Approach to Fuzzy Soil Mapping.
- Burke1, B. , MacCarthy2, S. and Petrovic1, R.(2001). Case-based Reasoning in Course Timetabling: An Attribute Graph Approach.
- Ralph, B. , Klaus-Dieter, A. ,Mirjam, M. , Meike, R. and Kerstin, B.(2009). Case-Based Reasoning. 20
- Roger, C. and Alex, K., Christopher, K.(2014). Inside Case-Based Explanation.
- Hans-Dieter, B. and Michael, M. On the Notion of Similarity in Case Based Reasoning and Fuzzy Theory.
- Watson and Kolodner, J. (2009).An Introduction to Case-Based Reasoning.
- Maggin, B.(2007). Clinical reasoning and its application to nursing: Concepts and research studies.
- Sima,S.( 2013) Case-Based Reasoning for Diagnosis and Solution PlanningCase-Based Reasoning for Diagnosis and Solution Planning.
- Hugh, O. and Derek, B. Models of Similarity for Case-Based Reasoning.
- Petri, M. & Henry, T. Bayesian Case-Based Reasoning with Neural Networks.
- Henry, P., Adam, W. and Katie, A. (2013). A formalization of argumentation schemes for legal case-based reasoning in ASPIC.Hinkle, D. and Toomey, C. (1995). Applying Case-Based Reasoning to Manufacturing.
- Hyowon, S. & Jae, H.(2008). Ontology-based Case-Based Reasoning (OntCBR) for Engineering Design. Iain, B.(2006). A case-based reasoning method for fixture design.
- Jaroslav, H., Jirı, D.(2008).Using case-based reasoning for mobile robot path planning.
- Juan A.(2013). jcolibri2: A framework for building Case-based reasoning systems
- Roth-Berghofer T.(2012), Building Case-based Reasoning Applications with myCBR and COLIBRI
- Rombo,O.(2013). Diabetes And Blindness In Kenya: A chronic diseases of nutrition in transition
- Kiberenge, M. (2010).Knowledge , Attitude and Practices Related to Diabetes Among Community Members in Four Provinces in Kenya: a Cross-Sectional Study
- Health Improvement Scotland (2013) Management of Diabetes A National Clinical Guideline
- World Health Organization .(2014).Available : http://www.who.int/features/2014/kenya-rising-diabwetes/en/.
Index Terms
Keywords