## 4 Preliminary Data Study  
Occasionally, a key feature becomes completely reliant on one or more related features and, as a result, the details in the data series (of two or more) are considering as equal, because treating them separately might make any accurate assessment too complicated. To this effect, establishing a correlation matrix might assist in ascertaining if any information is present that can be considered insignificant.  
Therefore, it is feasible to define the correlation factor between features, judged in pairs, by assuming that a positive relationship exists for any positive values. With this example, both the large and small values of a variable seem to be linked with the contrasting large or small values. So, a correlation factor with a negative value suggests a relationship that is negative or inverse.  
When two variables are encountered with a high correlation factor, a summarization that both offer the same details (therefore are insignificant) becomes likely, so they can be dismissed and the data measurements become less weighty and monotonous. To this effect, we used the R software [44]. R is a free software environment for math computing. R is a GNU project and provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques while being highly extensible.  
The _corplot_ routine [45], present in R software, is convenient for producing the correlation matrix that can illuminate the issue. As Figure 1 shows, relations can be expressed in pairs using this method, by utilizing the correlations between features.  
Using the data highlighted in Figure 1, establishing some deductions becomes possible. There is a strong connection between fast insulin and a meal because, more often than not, the two events happen at the same time. In any case, it could occur that in hyperglycemia there are exclusively insulin doses without food ingestion (in order to lower glucose levels) and, in hypoglycemia, the patient needs to ingest a light meal (for the most part, sugar) while insulin is never infused. With this, both fast insulin and a meal must be considered. However, be that as it may, with patients for the most part under good control, hypoglycemia and hyperglycemia ought to be something infrequent and less pronounced.  
Moreover, the relationship between glycemia and both insulin and meals are close to zero. This makes sense because a proper insulin dose completely compensates for hyperglycemia caused by ingestion. There is a predicted negative correlation found between blood sugar levels and exercise, as well as for heart rate. Moving on with the assessment, both exercise steps and heart rate are revealed as highly correlated, as expected. This association, however, is not all-encompassing as with some scenarios both tachycardia and bradycardia become possible under stress or sharp glycemic oscillations [46]. Plus, a clear inverse correlation is apparent when assessing heart rate and sleep, caused by the bradycardia during sleep. Similarly, such an inverse relation is identified between exercise and sleep, as can easily be predicted. Therefore, because the inverse correlation between heart rate and sleep is more robust than that between exercise and sleep, exercise should be viewed as an aspect that affects heart rate measurements. Due to this, exercise has been left out of the subsequent research.  
Notwithstanding, if an increase in heart rate is not correlated with physical activity, it could likewise be generated by stressful circumstances [47]. In addition, an increase in heart rate can be indicative of both hypoglycemia [48] and hyperglycemia [49] scenarios.