A Bio-Digital Expert System with Certainty Factor Reasoning for Early Kidney Disease Risk Assessment

Abstract

Introduction — Early identification of kidney disease risk is essential to prevent progression to severe renal impairment. However, uncertainty in symptom perception and limited access to transparent digital screening tools often hinder early assessment. This study proposes a bio-digital expert system incorporating enhanced Certainty Factor reasoning to support early kidney disease risk evaluation.

Methods — Early identification of kidney disease risk is essential to prevent progression to severe renal impairment. However, uncertainty in symptom perception and limited access to transparent digital screening tools often hinder early assessment. This study proposes a bio-digital expert system incorporating enhanced Certainty Factor reasoning to support early kidney disease risk evaluation.

Results — Evaluation across five structured scenarios demonstrated differentiated risk classifications consistent with symptom patterns and user confidence levels. The system produced graded outputs ranging from low to high risk, while minimal evidence scenarios correctly resulted in no significant risk classification.

Conclusion — The proposed system provides an interpretable, uncertainty-aware framework for early kidney disease risk assessment, emphasizing transparent digital decision support rather than clinical diagnosis.