At their onset, many medical conditions present a combination of overlapping and distinct symptoms, complicating early detection. To assist physicians in navigating these complexities and securing accurate diagnoses, this paper introduces a novel framework that maps the relationships between specific patient pathologies and broader disease categories. Central to this approach is the introduction of the sin-log distance method within Single-Valued Neutrosophic Sets (SVNS). By systematically accounting for data gaps and indeterminacy, this methodology overcomes the limitations and uncertainties of existing diagnostic tools. The practical application of this framework demonstrates its robust capability in accurately identifying illnesses and guiding effective clinical interventions.