An infant in the first 28 days following birth is referred to as a newborn baby. In Ethiopia, neonatal mortality is a serious problem that accounts for the lion's share of under-five mortality. Diagnosis and treatment of infant disease need specialized medical resources with plenty of expert knowledge and experience. Globally and particularly in low-income countries, there is a lack of such professional which make the diagnosis and treatment more difficult. The goal of this paper is to design a knowledge-based system for the diagnosis and treatment recommendation of neonatal diseases by collaborating with the knowledge obtained from machine learning and health experts. Design science research approach has been employed as the overall research design, and the hybrid data mining process model is used to extract knowledge from the collected clinical dataset. To this end, three classification algorithms in WEKA tools, namely, J48, PART, and JRip, were considered. Then, a partial decision tree (PART) algorithm under 10-fold cross-validation achieved the highest performance result with an accuracy of 98.06% and the researchers decided to use the generated rules for the development of a knowledge-based system. Evaluation results show that the developed prototype registers 90.9% accuracy in system performance testing and 89.2% in user acceptance testing. In conclusion, the system is used as an assistant tool for healthcare experts and could be effective if it could be implemented. [ABSTRACT FROM AUTHOR]