The clustering techniques, combined with distance-based similarity measures of Single Valued Neutrosophic Sets (SVNS) are studied and applied in medical diagnosis. The study starts with reviewing SVNS' theoretical foundations, emphasising its ability to capture and handle ambiguous data. This study focuses on integrating distance-based similarity measurements to improve the clustering process, which has seen limited implementation thus far. The set of data includes three patients with five symptoms and three diagnoses. To deal with the data in medical diagnosis, each patient is diagnosed with a disease based on distance-based similarity measures. The disease with the highest similarity measure value indicates the recognized disease for that patient. Then, the diseases are clustered into different categories depend on the values of confidence level. The obtained results show that the suggested method enhances the precision of medical diagnosis significantly, especially in cases with ambiguity and uncertainty.
