acute abdomen is one of the emergency diseases which is a sudden pain and cannot give a chance for follow-up treatment. This type of disease needs accurate and timely or immediate treatment. Otherwise the pain going to be complicated or leads to death. However, in developing country acute abdominal patients had surgical operation after waiting long period of time. This time delay and other factors have been t*he case of an increased mortality and morbidity rate. Behind time delay, other major challenge to diagnose patients in developing county is lack of skilled man power, tiredness, lack of knowledge sharing between professionals. The factors are the challenges that affect the quality of health care service in hospitals and reduce the quality of decisions made by physicians in the domain area. As a result, main objective of the study was to investigate the applicability KBDSS using integration of rule based and case-based reasoning approach so as to improve the quality of decision made by domain experts, to provide effective and efficient services to the patients and to improve shortage of human expert in specific domain area. To achieve this objective, domain knowledge is acquired using semi-structured interview technique is implemented. Domain experts are selected from Felege hiwot referral hospital in Bahir-Dar using purposive sampling instrument. In addition, secondary data is acquired from different sources such as journal articles, health care guidelines, manuals, books and different websites. The conceptual model of the knowledge-based system used a decision tree structure which is easy to understand and interpret the procedures involved in patient diagnoses. Based on the conceptual model, the prototype is developed with SWI prolog and java software tools. The performance of the system has got good acceptance by the system evaluators. According to the system evaluators, the accuracy of the system beyond the domain expert were 99% acute abdominal cause classification and severity level identification were 66% of accuracy. In the evaluation the system that classified the attribute according to the target problem was 71.33% accuracy and in the comparison of the three reasoning approaches hybrid approach was recommended for health care DSS with highest accuracy value compared with rule-based and case-based reasoning approaches which were 87.66 % of accuracy value. automation of KBDSS has a high contribution for establishment of truthful decision making in acute abdomen patient’s treatment.