# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 6 Conclusions  
In this research work, an innovative VLC-based method for detecting COVID-19 in a subterranean environment was proposed. It was found that the unfavourable effects of the underground channel on VLC communications can be mitigated through precise mathematical modelling of the underground channel.  
In order to get a higher spectrum efficiency for longer chromosomes, it has been shown that square constellations should be favoured due to their superior separation qualities within a photon stream. Transmission of monochromatic photon streams is an additional alternative. Since no energy is expended on phase incoherence effects, which generally result in self-destructive phase effects, when coherent monochromatic frequencies are employed, the results improve. It was also established that employing a laser saves money because there is no need for white light amplification equipment, often known as a driver. In addition, it was revealed that mathematical scaffolding in the exponential representation of DNA, in conjunction with the novel modulation and suitable channel modelling, prevents the transmission of heavy images.  
The XGBoost technique was found to be the most successful, since it generates a large number of trees and is the only one that can learn a nonlinear data set by creating a novel goal function. When \(N=2^{2^{i}}\times 2^{2^{i}},i=0,1\) points are used. For example, the dataset is too small for the algorithm to learn to predict future values, despite a slight improvement when \(N=2^{2^{i}}\times 2^{2^{i}},i=1\) points are used. As the dataset expands in size, the gain increases dramatically; this is something that linear models cannot achieve. For BER = \(10^{-3}\), gains of \(-10\) [dB], \(-3\) [dB], \(3\) [dB] and \(5\) [dB] were achieved for \(N=2^{2^{i}}\times 2^{2^{i}},i=0,1,2,3\), respectively. The conclusion is that the square constellation \(N=2^{2^{i}}\times 2^{2^{i}},i=3\) yields a greater profit. During the searching phase, a classification algorithm was selected from a pool of available options. For a total of 630 COVID-19 samples, the best model was XGBoots, which displayed an accuracy of 96.03% and a _recall_ rate of 99% for positive values, placing its performance above that of the other models.  
Figure 21: Results of the application of XGBoots.  
Furthermore, the uncertainty in the data propagates to the channel, so the accuracy of the trained models is determined by the criteria employed by the expert who creates the dataset. Clearly, the only way to rectify this is to compile a dataset from multiple sources so that it is complete and objective.  
By extracting genetic information more efficiently, it is possible to classify the bands present in electrophoresis samples by using ML and a three-state classification process to determine whether the band corresponds to a COVID-19 positive, negative or ladder sample.  
Conceptualisation, I.S. and R.Z.-I.; methodology, I.S. and D.Z.-B.; software, R.Z.-I.; validation, I.S., W.A. and V.G.; formal analysis, I.S., R.B. and P.P.J.; investigation, I.S. and R.Z.-I.; resources, I.S.; data curation, V.G.; writing--original draft preparation, I.S., W.A, V.G., R.B and M.I.; writing--review and editing, I.S., R.Z.-I. and M.I.,visualization, I.S.; supervision, I.S., C.A.-M. and M.I.; project administration, I.S. and C.A.-M.; funding acquisition, I.S. and C.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Project Dicyt-062117 SG, FONDEF No. ID21110191, FONDECYT No. 1211132, STIC-AmSud 22-STIC-01, and BECAS DE MASTER NACIONAL ANID No. 22220262 in Chile.  
Not applicable.  
To the support of the University of Santiago's Postgraduate Vicerectomy and Master's Program in Engineering Sciences in Electrical Engineering.  
The authors declare no conflict of interest.