Understanding Public Health through Decision Tree Classifier

Introduction
In the United States, public health has been a topic of concern, especially in the areas of Diabetes, Obesity, and Physical Inactivity. Understanding the risk factors at a granular level, such as the county level, can help policymakers take more effective action. This post explores a Decision Tree Classifier model that classifies U.S. counties into different risk categories based on these three health metrics.

Methods
Data Preprocessing:
We focused on three key metrics: rates of Diabetes, Obesity, and Physical Inactivity.

Decision Tree Classifier:
A Decision Tree Classifier was chosen for its simplicity and interpretability. Decision Trees work by splitting the data into subsets based on feature values, aiming to have each subset as pure as possible, meaning they contain instances of only one class.

Results
Model Performance:
The Decision Tree Classifier achieved an overall accuracy of 94%, suggesting a high level of reliability in classifying counties into different risk categories. The weighted averages for precision, recall, and F1-score were 95%, 94%, and 95%, respectively, further solidifying the model’s robustness.

Cluster-wise Insights
Cluster 0: High-Risk Counties
Precision, Recall, F1-score: All metrics scored a perfect 100%.

Insight: This indicates that the model is extremely reliable for identifying counties in the high-risk category. Targeted interventions for these counties could be planned with high confidence.

Cluster 1: Moderate Risk, Low Activity
Precision: 97%   Recall: 91%   F1-score: 94%

Insight: The model performs very well for this cluster but leaves a minor room for improvement in Recall. Counties in this cluster might benefit from more focused health initiatives.

Cluster 2: Balanced Risk Counties
Precision: 95%   Recall: 100%   F1-score: 97%

Insight: The model is nearly perfect in classifying counties in this balanced risk cluster, which suggests that these counties may already be on the right track in terms of public health.

Cluster 3: Healthiest Counties
Precision: 33%   Recall: 50%   F1-score: 40%

Insight: Despite the lower scores, the sample size for this cluster is very small (only 2 instances in the test set), making these metrics less reliable for drawing strong conclusions. More data might provide a more accurate representation.

Discussion
Insights:
The high accuracy and performance metrics for most clusters suggest that the model could be used effectively for county-level public health risk assessment. High precision and recall rates for the riskier clusters (0, 1, and 2) mean that public health interventions can be more targeted and likely more effective.

Limitations and Future Work:
One limitation of the study is the small sample size for Cluster 3, which makes the model’s metrics less reliable for that group. Future work could involve gathering more data or employing techniques to handle class imbalance.

Conclusion
The Decision Tree Classifier model shows promise as a tool for assessing public health risks at the county level. Its high accuracy and robust performance metrics across multiple clusters indicate its potential utility in guiding targeted public health interventions.