Conclusion

This small project demonstrated the value of leveraging metabolic profiles and related calculations in a synthetic dataset. This type of analysis greatly expands upon the binary Metabolic Syndrome classification. The potential of Metabolic Syndrome variables is utilzed to a far greater extent than the simple binary classification model allows. Changes to BMI status, number of risk factors and changes to the health risk score (along with a bar graph for visualization) are the chosen key metric points. This is only one example of how individual metrics can be leveraged with comparison to large dataset profiles and related analysis. Additional profile predictions could include a large variety of metric comparisons and additional novel risk calculations associated with the data set. Similar analysis with real world datasets can be leveraged in a similar manner.