The aim of this study is to perform a simulation of large scale Wireless Sensor Networks (WSN) working on medical diagnosis. The main feature of this WSN is its density and the fact that all sensors are in the same radio range. Our approach helps medical staff to diagnose diseases in an automatic way. In this context, each patient is equipped to a set of sensors. Each sensor is connected to a transducer dedicated to measure a specific health parameter . This WSN will perform some computations and run an alarm when some diseases are suspected. This technique is based on the population protocol to handle data exchanged between sensors. This approach is in fact an efficient distributed algorithm which implies that the diagnose may be done by any sensor dealing with the disease detection. The main result of this paper is: the method supports large scalability which proves its practicability for real cases.