Resumen
This paper presents the utilization of the Data Analysis Smart System (DASS) of ARMNANO in a nanotechnology application in electronic health. We made a special approach to the liver situation for patients that have been monitored with respect to two variables concerning their liver status: the Mean Corpuscular Volume (MCV) and the Alkaline phosphotas (ALKPHOS). These variables are analyzed using the autonomous cycle ?Conditioning Thinking Mode? (CTM), one of the two autonomic cycles of data analysis tasks that make up the DASS. In this sense, an optimization problem is defined to determine the optimal deployment of nanosensors (NSs) for the proper determination of liver status. The application of genetic algorithms (GA) allows us to find the optimal number of NSs in the system to precisely determine the liver status, avoiding a large data volume. In total, we evaluated its implementation in two case studies and carried out a hyperparameterization process for assuring the definition of the key parameters. The greatest propensity is to place NSs in the regions close to the liver, becoming saturated as the amount of SNs increases (they do not improve the quality of the liver status value).