# Results

```{r}
#| include: false
#| cache: true

library(magrittr)
load("test.Rda")

```

The final data set used for this analysis consisted of 11,340
observations. All observations contained a TSH and Free T4 result and
less than three missing results from all other analytes selected for the
study. The dataset was then randomly split into a training set
containing 9071 observations and a testing set containing 2269
observations. The data was split using stratification of the Free T4
laboratory diagnostic value. @tbl-strata shows the split percentages.

```{r}
#| label: tbl-strata
#| tbl-cap: Data Stratification 
#| echo: false

strata_table %>% knitr::kable()

```

First, the report shows the ability of classification algorithms to
predict whether Free T4 will be diagnostic, with the prediction quality
measured by Area Under Curve (AUC) and accuracy. Data regarding the
univariate association between each predictor analyte and the Free T4
Diagnostic value is then presented. Finally, data is presented with the
extent to which FT4 can be predicted by examining the correlation
statistics denoting the relationship between measured and predicted Free
T4 values.

## Predictability of Free T4 Classifications

In clinical decision-making, a key consideration in interpreting
numerical laboratory results is often just whether the results fall
within the normal reference range [@luo2016]. In the case of Free T4
reflex testing, the results will either fall within the normal range
indicating the Free T4 is not diagnostic of Hyper or Hypo Throydism, or
they will fall outside those ranges indicating they are diagnostic.