Implementation of a Mobile-Based Expert System for Anesthesia Type Recommendation Using Bayes’ Theorem - Siregar, Siti Julianita (Universitas Islam Sumatera Utara, 2025)
The selection of anesthesia type is a critical stage in surgical procedures that must consider the patient’s clinical condition and risk level. Inappropriate anesthesia selection may increase perioperative complications. This study aims to implement a mobile-based expert system to recommend anesthesia types using the Bayes Theorem based on the American Society of Anesthesiologists (ASA) classification. The system was developed by constructing a knowledge base consisting of anesthesia classifications, patient symptom data, and their relationships. System evaluation was conducted by comparing the system’s recommendations with expert decisions. The results indicate that the expert system provides accurate anesthesia recommendations. These findings demonstrate that the Bayes Theorem is effective in handling uncertainty in clinical data, and the mobile-based expert system can serve as a decision support tool for anesthesia selection.