Diagnostic Expert System Website-Based Stroke Disease Using Forward Chaining and Certainty Factor Methods

Abstract

Background: Stroke is a neurological condition characterized by the sudden loss of brain function resulting from disruption of blood supply to the brain. It ranks as the second leading cause of death globally, with a mortality rate ranging from 18% to 37%, and constitutes a major cause of neurological disability in Indonesia as well as the third leading cause of death worldwide.
Objective: This study aimed to develop a web-based expert system enabling patients and their families to perform early detection of stroke symptoms.
Method: This study employed a prototype-based development methodology. The knowledge base was constructed through structured interviews with a neurologist and validated through cross-checking with clinical records. The Forward Chaining method served as the inference engine, deriving diagnostic conclusions from symptom-based facts, while the Certainty Factor method quantified diagnostic uncertainty. System testing was conducted using six patient case samples provided by the expert.
Findings and Implications: The system achieved a diagnostic accuracy of 86.68% based on cross-validation with expert knowledge using six clinical case samples. Black-box functional testing confirmed that all system features performed as expected.
Conclusion: These results indicate that the system is capable of supporting preliminary stroke symptom assessment, thereby facilitating early decision-making prior to professional medical consultation. However, given the limited number of test cases, the system’s generalizability warrants further validation using a larger clinical dataset.