Hybrid Manhattan Distance-Certainty Factor for Early Cardiovascular Diagnosis in a Hospital - Tomatala, Michel (IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., 2026)
Cardiovascular disease drives high mortality and operational strain in public hospitals, underscoring the need for tools that standardize rapid early decisions. We evaluated a hybrid expert system that integrates Manhattan Distance (MD) for case-based similarity with a Certainty Factor (CF) framework for rule-based evidence aggregation. Using a locally curated knowledge base (110 cases, 69 symptoms, 13 conditions) and a 26-case hold-out against specialist references, the system retrieves nearest cases via MD on symptom vectors and then computes per-diagnosis confidence with CF. The system achieved 23/26 exact matches (accuracy 88.46%), with confidence values spanning 71.90–99.99% higher when nearest-case patterns and rules converged and moderated in ambiguous presentations (e.g., suspected aneurysm). Outputs were interpretable and suitable for 15–30-minute consultations, supporting consistent triage where specialist capacity is limited. These findings suggest a practical pathway to improve timeliness and reduce variability. Future work should pursue multi-site validation, knowledge-based expansion for atypical phenotypes, and governance for safe, equitable deployment.