This study introduces the nano simply alpha open set and proposes a new approximation space extending Pawlak's approximation. This new space includes the nano simply alpha lower and nano simply alpha upper approximations, denoted by specific notations, offering a refined framework for analyzing data. The ζ nα-lower and ζ nα-upper approximations for any set Also, we study nano ζ nα-rough approximation. Those investigations look at the connections between various approximation types and their characteristics, proposing methods applicable to medical diagnosis and other decision-making fields. These methods provide deeper data insights, enhancing precision and reliability in complex problem-solving. We introduce a "general neighborhood" concept, expanding on Pawlak space with a general upper and lower approximation. A case study for chronic kidney disease demonstrates the effectiveness of these methods in identifying critical symptoms. Additionally, an algorithm a, supports application for any number of patients or decision-making issues.