Abstract
Chronic kidney disease is an important challenge for health systems around the world and consuming a huge proportion of health care finances. Around 85% of the world populations live in developing country of the world, where chronic kidney disease prevention programs are undeveloped. Treatment options for chronic kidney disease are not readily available for most countries in sub-Saharan Africa including Ethiopia. Many rural and urban communities in Ethiopia have extremely limited access to medical advice where medical experts are not readily available. To address such a problem, a medical knowledge-based system can play a significant role. Therefore, the aim of this research was developing a self- learning knowledge based system for diagnosis and treatment of the first three stages of kidney disease that can update the knowledge without the involvement of knowledge engineer. In the development of this system, the following procedures are followed: Knowledge Engineering research design was used to develop the prototype system. Purposive sampling strategies were utilized to choose specialists. The information was acquired by using both structured and unstructured interviews and all knowledge’s are represented by using production rule. The represented production rule was modeled by using decision tree modeling approach. Implementation was employed by using pro-log tools. Testing and evolution was performed through test case and user acceptance methods. Finally, we extensively evaluate the prototype system through visual interactions and test cases. Finally, the results show that our approach is better than the current ones.
Highlights
Chronic kidney disease is an important challenge for health systems around the world and consuming a huge proportion of health care finances
For many years the treatment and diagnoses chronic kidney disease in Ethiopia has not been studied and there is no national strategy for prevention and care of patients with chronic kidney disease
Production rule Knowledge representation techniques are used in which knowledge is represented in the form of condition-action pairs: In the same way, the rules that contain the stages of kidney diseases, major symptoms of kidney diseases, Glomerular filtration rate (GFR) laboratory values, family history of kidney, diabetes, and cardiovascular, and high blood pressure diseases are formulated in the following ways: Rule 1: IF
Summary
Chronic kidney disease is an important challenge for health systems around the world and consuming a huge proportion of health care finances It is even more significant for developing countries which face the double burden of infectious diseases and growing problems of noncommunicable diseases such as cardiovascular, diabetes and hypertension [1]. A knowledge-based system is a software system that contains a significant amount of knowledge in an explicit, declarative form This has been replaced by methodological approaches that have many similarities with general software engineering practice. KBS development is best seen as software engineering for a particular class of application problems. These applications problems typically require some form of reasoning to produce the required results.
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