Expert System for Disease Diagnosis in Pregnant Women Using Backward Chaining Method - Yosefin Caltrin Odelia; Patmawati Hasan; Emy Lenora Tatuhey (International Journal Software Engineering and Computer Science (IJSECS), 2025)
Healthcare providers treating pregnant women need reliable tools for accurate disease diagnosis. We created an expert system that assists doctors in identifying illnesses during pregnancy through backward chaining inference methods. The project involved collaboration with medical professionals at North Jayapura Health Center who shared their clinical expertise to develop the system. Our backward chaining method examines patient symptoms by working backward from potential diseases to find diagnostic matches. Through detailed interviews, healthcare professionals contributed their years of experience treating pregnant patients. We transformed their knowledge into diagnostic rules that help the system recommend possible diagnoses based on symptom patterns. The system serves as a decision support tool that helps doctors make quicker, more confident diagnostic choices. Many pregnancy-related diseases share similar symptoms, which can confuse even experienced practitioners. Our tool helps sort through these diagnostic challenges while preserving the doctor's authority in making final decisions. Evaluation showed the expert system significantly improved diagnostic performance in primary care settings. Many healthcare facilities lack access to specialists familiar with pregnancy complications. The system brings expert-level diagnostic knowledge to these underserved areas, improving patient care quality