Developing a Knowledge-Based System for Diagnosis and Treatment Recommendation of Neonatal Diseases Using CLIPS

International Journal of Academic Engineering Research (IJAER) 7 (6):38-50 (2023)
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A newborn baby is an infant within the first 28 days of birth. Diagnosis and treatment of infant diseases require specialized medical resources and expert knowledge. However, there is a shortage of such professionals globally, particularly in low-income countries. To address this challenge, a knowledge-based system was designed to aid in the diagnosis and treatment of neonatal diseases. The system utilizes both machine learning and health expert knowledge, and a hybrid data mining process model was used to extract knowledge from a clinical dataset. The PART algorithm achieved the highest performance result with 98.06% accuracy under 10-fold cross-validation, and the generated rules were used to develop the knowledge-based system. The system achieved 90.9% accuracy in system performance testing and 89.2% in user acceptance testing, and is intended to serve as an assistant tool for healthcare experts.

Author's Profile

Samy S. Abu-Naser
North Dakota State University (PhD)


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