<![CDATA[Coronary Heart Disease (CHD) remains a global health challenge, yet health literacy gaps often make it difficult for the general public to understand clinical parameters and diagnostic uncertainty. In an effort to support social welfare and digital health accessibility, this study developed an expert system prototype that translates complex clinical data into easily understandable CHD risk levels (Low, Moderate, High). Utilizing the Certainty Factor (CF) method, the system is designed not as an independent clinical screening tool, but as a transparent, self-educational platform for non-specialist users. Evaluated on the extended UCI Heart Disease dataset (920 patient records), the CF approach successfully provided clear, explainable reasoning for its risk classifications. Although the initial algorithmic evaluation highlighted the limitations of static rules in handling the non-linear variability of medical data (achieving 46.30% accuracy), these findings emphasize that the primary value of digital health technology for the general public lies beyond mere computational accuracy. Instead, its significance is rooted in communication transparency, improving health information accessibility, and raising health awareness.]]>
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