Amharic based Knowledge-Based System for Diagnosis and Treatment of Chronic Kidney Disease using Machine Learning

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, 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 on 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 developed prototype system. Purposive sampling strategies were utilized to choose specialists. The information was acquired using both structured and unstructured interviews and all knowledge's are represented using production rule. The represented production rule was modeled by using decision tree modeling approach. Implementation was employed using pro-log tools. Testing and evolution was performed through test case and user acceptance methods. Furthermore, 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.