Knowledge-based system for the diagnosis and treatment of hypertension

BACKGROUND: Diabetes is a disease that affects the body’s ability to produce or use insulin. A total of 425 million people are suffering from diabetes in the world. Of this, more than 16 million people live in the Africa Region, which is estimated to be around 41 million by 2045. The main objective of this study was to design and develop a prototype knowledge-based system using data mining techniques for diagnosis and treatment of diabetes.METHODS: For this study, experimental research design was employed, and the researchers used domain expert knowledge as a supplement of data mining techniques whereby three classification algorithms in WEKA; namely J48, PART and JRip were used, and finally the researchers decided to use the results of J48 classification algorithm. Ultimate Visual basic studio 2013 (Vb.net) was used to store knowledge and as front side of prototype. Common lisp prolog (Clisp) was used for obtained knowledge back end coding.RESULTS: Using a decision tree algorithm; namely J48, 2512 (95.1515%) of the instances were classified correctly, and 128 (4.8485 %) were classified incorrectly. The second most performing model was generated by JRip Classier. This model scored the 94.7348% accuracy on the general data to classify the status of diabetic patient datasets. It classified the 2501 instances of the records correctly.CONCLUSION: The J48 model was the best performing model with the best accuracy of results.

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Developing a Knowledge-Based System for Diagnosis and Treatment of Malaria

Journal of Information & Knowledge Management ◽

10.1142/s0219649216500362 ◽

2016 ◽

Vol 15 (04) ◽

pp. 1650036 ◽

Cited By ~ 2

Author(s):

Chala Diriba ◽

Million Meshesha ◽

Debela Tesfaye

Keyword(s):

Public Health ◽

Health Problem ◽

Health Professionals ◽

Health Centre ◽

Public Health Problem ◽

Diagnosis And Treatment ◽

Prototype System ◽

Domain Experts ◽

Knowledge Based System ◽

Knowledge Based

Malaria is a serious and fatal disease caused by a parasite that can infect a certain type of mosquito which feeds on human blood. It is a public health problem in Ethiopia and a major cause of illness and death. More than 75% of the total land of Ethiopia is malarious affecting more than 68% of the population, making malaria the leading public health problem in Ethiopia. In an effort to address such problems, it is important to develop knowledge-based system (KBS) that can provide advice for health professionals and patients to facilitate diagnosis and treatment of malaria patients. Experimental research design was used to developed prototype system. Purposive sampling technique was used to select domain experts for knowledge acquisition. The domain experts are selected from Jimma special hospital, Adama hospital and Agaro health centre. The knowledge was acquired using both structured and unstructured interviews from domain experts and represented by production rule, (if- then method). The user's acceptance of the prototype system by visual interaction method that by showing the prototype system to the domain experts was conducted result is 83.21%. In addition, performance of the prototype system was evaluated using case testing method and produce result of 82.3%. It is promising to save the life of people in rural area where there is scarcity of health professionals and apparatus. In addition, it is possible to reduce time and cost of diagnosis and treatment in health centre by implementing intelligent systems. Developing in local languages, good interface programming language and in other techniques are the future works of the study.

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