<s>[INST] To refine its estimates on cardiovascular disease, Country "Healthsinia's" health organizations decided that they need to calculate each citizen's 10-year risk of developing cardiovascular disease. Healthsinia's organization teamed up with a renowned data scientist who implemented a machine learning model to come with risk scores. The data scientist selected a dataset of 100, 000 citizens which comprised 74, 220 females and 25, 780 males. The model estimated the following risks:

- Females: 0.095
- Males: 0.150

Let's assume the data scientist used a 5-segment risk threshold to grade the risks. Six (6) percent of the population fall under category 1, with an extremely low risk of cardiovascular disease (0-0.049). Seven (7) points fall under the category 2, with a low risk (0.05- 0.059). Five (5) percent of the population fall under the category 3, with a moderate risk (0.06-0.100). Eleven (11) percent fall under the risk category 4 with high risk (0.101-0.149), whilst sixteen (16) percent fall under the risk category 5, with an extremely high risk \(\geq\) 0.15.

To make sense of this data, lets use the actual categorized risk and the predicted risk.
To be more precise, to make sense of these data, we need to cross-tabulate the estimated data by risk levels with the actual risk values from the data to determine the performance of the model overall. The cut-off values of each category are:
\(\bullet\) Category 1: 0-0.049
\(\bullet\) Category 2: 0.05-0.059
\(\bullet\) Category 3: 0.06-0.100
\(\bullet\) Category 4: 0.101-0.149
\(\bullet\) Category 5: \(\geq\) 0.15

Can you help me perform the calculations?[/INST]