Context

MediScan, a digital health company, uses a binary classification model to flag patients for follow-up diabetes screening after an annual checkup. The current debate is whether to deploy a model version with higher precision or one with higher recall, because missed high-risk patients can delay treatment, while too many false alarms overwhelm clinicians.

Current Performance

Two candidate logistic regression models were evaluated on the same validation set of 20,000 patients, with 1,000 actual positive cases.

The Problem

The clinical operations team can handle at most 1,400 follow-up screenings per month. Each unnecessary follow-up costs about $40 in staff time and patient outreach. Each missed high-risk patient is estimated to create $900 in downstream medical and retention costs.

Requirements

1. Compare Model A and Model B using the provided metrics and explain the tradeoff clearly.
2. Determine which model you would recommend under the stated business constraints.
3. Use the confusion matrix implications to quantify false positive and false negative impact.
4. Explain when a higher-precision model would be preferable and when a higher-recall model would be preferable.
5. Recommend one or two next steps to improve the chosen model or tune its threshold.

Constraints

• Monthly follow-up capacity cannot exceed 1,400 patients.
• Patient safety is more important than outreach efficiency, but budget still matters.
• The team prefers a simple threshold change over retraining if performance is close.