###### Abstract  
This paper presents the utilization of the Data Analysis Smart System (DASS) of ARMANO 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). automatic computing; data analysis; combinatorial optimization problem; nanotechnology Article  
1  
A Note that the authors declare that the  
publication regarding the data analytics focus. To this end, we employ the Autonomic Cycles (ACs) defined in the Data Analysis Smart System (DASS) [5] of ARMNANO [6]. Herein, the data analysis can be carried out by any of the three ACs, depending on the situation of the context. The three ACs are the Conditioning Thinking Mode (CTM), the Optimizing Thinking Mode (OTM), and the Fixing Thinking Mode [5], which define a complete framework for the treatment and analysis of the nanodata collected by the system. The eHealth applications are not a new area to study, but the implementation of nanosensors and nanoactuators is a novelty. [PERSON] et al. [7] defined a health informatic architecture incorporating information from either nanodevices or not. On the other hand, [PERSON] et al. [8] designed an architecture embedding an NS device set. However, in these previous works, there is no analysis of the NSs to be deployed (how many and where) depending on the problem to be solved. For ARMNANO, the deployment of NSs is a problem to be solved, for which it requires mechanisms that allow for optimizing the use of NS. For the data analysis handled by ARMNANO, the adequate capture of the nanodata (site, frequency and quality, among other things) is required in such a way to generate the appropriate solutions based on those collected data analyzed by integrating them with other nanodevices. Thus, this work is dedicated to the determination of the NS that should be deployed in a medical context, in order to later be able to perform analysis tasks of these nanodata using ARMNANO. This is a combinatorial optimization problem that, in our case, is intended to be solved using genetic algorithms (GAs) [9; 10; 11; 12]. Specifically, DASS solves this optimization problem using the CTM-AC. We test our approach in a case study to monitor the liver, in order to determine its status [13; 14; 15]. The research questions posed in this study are: Is it possible to perform data analysis tasks to meet the context objectives using nanodata? From there, is it possible to define the NSs required to capture the nanodata in data analysis tasks? Thus, the contributions of this work are: (i) the definition of an intelligent system based on nanotechnology for data analysis for e-health applications; (ii) the definition of an approach based on GA to determine the NSs that should be deployed in a medical context for nanodata analysis tasks; (iii) the definition of a nanodata-based approach to monitor the state of a liver. This paper is organized as follows: it presents the related works in Section 2. Section 3 is presents ARMNANO and its ACs.