Overview

You are a data and business intelligence analyst working with a network of hospitals in rural districts. The hospital’s chief of behavioral health services (CBHS) would like to use existing research data to create a model to predict or classify newer patients as at-risk or not at-risk for clinical depression. Such classification predictions will enable the hospital to provide early mental health interventions to at-risk individuals. The CBHS has tasked your team with creating the predictive classification model and then testing the model on five new patients at the hospital.

So far, you have applied different predictive algorithms on the data set and presented your findings to the CBHS. Now, you want to analyze the data further and determine which factors have the strongest and least influence on a patient’s risk of depression. This analysis will help behavioral health services team design better and more targeted early intervention programs.

In this assignment, you will generate a variable-importance chart to identify which independent variables have the strongest influence on the output from a prediction model.

For this assignment, you will use the output values from the logistic regression you performed in Module Two using Excel. Ensure that you have the final regression results, including any updates you may have made based on your instructor’s feedback.

Directions

Write an executive summary to identify the importance of different variables in the prediction model and how they may impact the business decisions. Include relevant screenshots from Excel to support your conclusions.

Specifically, you must address the following criteria:

• Variable-Importance Charts: Create variable-importance charts to compare the r2 coefficients for each independent variable. Compare variables on the same scale and include relevant screenshots from Excel.
  • Create bar graphs of the r2 coefficient values in the logistic regression results table you generated in Module Two.
  • Create at least two variable-importance charts to compare variables on the same scale.
    • Create one chart to compare financial variables and one to compare demographic variables.
• Implications: Discuss the implications of the variable-importance charts on the forecast model.
  • How are the different variables in the data set likely to affect a patient and their risk of depression?
    • Which factors or parameters in a patient’s profile are most likely to affect their risk of depression?
    • Which factors are likely to not influence the predictions about risk of depression?
  • How did you arrive at these conclusions?
• Recommendations: Use the information from the variable-importance charts to make recommendations for the business requirement.
  • Make at least two recommendations to the behavioral health services team about risk factors to focus on when designing early mental health intervention programs.
    • Justify your recommendations based on your analysis.
    • How will your recommendations add business value to the programs?