Diabetes Detection and Care Applying CBR Techniques
Mukesh Kumar Jha1, Debanjan Pakhira2, Baisakhi Chakraborty3
1Mukesh Kumar Jha, Department of Information technology, National Institute of Technology, Durgapur, West Bengal, India.
2Debanjan Pakhira, Department of Information technology, National Institute of Technology, Durgapur, West Bengal, India.
3Dr. Baisakhi Chakraborty, , Department of Information technology, National Institute of Technology, Durgapur, West Bengal, India.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 32-37 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1124112612/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diabetes is a lifelong (chronic) disease increase at a rapid rate because of sedate life style, changes into urban culture, unhealthy foods and lacking of physical activity. It is an incurable chronic disease, but through true diabetes screening and advanced sugar monitoring can prevent risky complications. A little information, Precaution and absolute care plan can go a long way to dealing with diabetes. It is very hard to make an excellent care plan and maintaining healthy blood glucose level for patients and their health care providers. In this research work we proposed a case base decision support system for patients with diabetes. Case based reasoning is an artificial intelligence technique to detect diabetes and its type, its seriousness and giving the appropriate care plan. This system helps doctors and patients to check, analyze and repair solutions. A case consists of a problem description (e.g. symptoms) and a solution (e.g. a care plan and a therapy). Cases are stored in a database of cases called case bases. To solve an actual problem a notion of similarity is used to retrieve similar cases from case bases. The solutions of these found similar cases are used as starting points for solving the actual problems at hand. The system analyzes the symptoms of the patients and gives the exact types of diabetes, its seriousness level and the appropriate care plan for appropriate patients. If it is not found then system generates basic care plan by ontology. After that system modified that case and stored in its ever expanding database for future use. The learning process of CBR is retaining the modified solved case in the data base is gives a big scope to solve new problems in future.
Keywords: Case-Based Reasoning, Detection, Diagnosis, Ontology.