## 2 Related Works  
[PERSON] et al. [16] proposed several NSs for biosensing with machine learning algorithms for the in situ and on-time detection of unknown diseases. They proposed NSs such as those based on Fluorescence, Surface Enhance Raman Scattering, and electrochemical, etc. The authors proposed a smart design of these NSs, depending on the environment, required sensitivity, target disease and experimental technique. [PERSON] et al. [17] carried out a study of the utilization of genetic algorithms for the definition of nano-architectures. They define three main approaches, one of them based on genetic algorithms: the form-finding strategies helped by genetic algorithms, the hybrid designs with nanomaterials or nanosensors, and the living architectures. [PERSON] et al. [18] designed an Intelligent Healthcare Data Management System for the human body. The summarized design comprises a mobile device that connects to the NSs and reads the measurements; this information then is sent to the PC server, and the server acknowledges the mobile. The overfitting of the machine learning algorithms has been addressed through the use of GAs to perform a feature selection. [PERSON] [19] found that a Logistic Linear Regression applied to determine the state of health on a large-scale healthcare database is better if a variable selection is made through a genetic algorithm. In their work, the GA determines the most important variables as age, diabetes, kidney and affectation, among others. In addition, the GAs were applied by [PERSON] et al. [20] to find out the best Metal-OrganicFramework (MOF) combination in a gas NS in order to discriminate between O\({}_{2}\), CO\({}_{2}\) and N\({}_{2}\). GA studied 961 gas mixtures and the best combination contemplated a remarkable structural diversity such as pore sizes, and surface area, among others. [PERSON] et al. [21] report the varied applications of GAs in medicine, in areas such as radiology, radiotherapy, rehabilitation, etc. The applications came mostly from disease detection and diagnosis, treatment planning, among others. For instance, in radiology, they found that it is applied in the detection, segmentation and classification of normal and pathological patterns appearing in magnetic resonance, compute tomography, etc. In addition, the research of [PERSON] et al. [22] applied GAs in medical diagnostics, for instance, in the understanding of ECGs, plaque of the thorax, or any kind of imageology taken on the patient. [PERSON] et al. [23] analyze several NSs, specifically lab-on-a-chip biosensing devices such as plasmonic sensors, electronic tongues and colorimetric sensors coupled to smartphones. These NSs can be used in big data analysis for classification problems in the context of wearable and handled biosensing platforms for healthcare systems. The work of [PERSON] et al. [24] proposes a monitoring healthcare system based on the Internet of nanotechnology (IoNT). In particular, IoNT can be used for diagnosis and treatment, as well as e-health monitoring, among other applications. This work explores these applications. [PERSON] et al. [25] describe a personalized healthcare management-related analytical tool based on biosensors for early-stage disease detection. They use the low-level detection of a targeted disease biomarker (pM level) to evaluate the progression of the disease under therapy. This information is collected and analyzed in multi-aspects to determine the effectiveness of a prescribed treatment, optimize therapy, and correlate the biomarker level with the disease pathogenesis. The work [26] discusses the applications of IoNT in the context of healthcare issues, and highlights the steps undertaken to uplift healthcare in India. They analyze how nanomedicine, along with IoNT, would change the very basis of investigating, curing, and preventing diseases. In particular, they remark that e-health systems will allow more customized, well-timed, and convenient as well as cost-effective, investigation, medication, and tracking of health. [PERSON] et al.