Tradeoffs in Machine Learning: Precision vs. Recall in Healthcare #

This guide summarizes two key scenarios in healthcare where we might prefer:

1. High Precision but Lower Recall
2. High Recall but Lower Precision

(1) High Precision, Lower Recall #

✅ When to Use: #

• When false positives are costly or harmful
• When resources are limited
• In early screening/filtering stages

📌 Justification: #

• You want to be very confident before taking action.
• Missing some real cases is acceptable if wrongly flagging someone leads to emotional, financial, or clinical harm.

💡 Examples: #

• Genetic Testing for Rare Diseases: Only flag patients when you’re very sure. A false positive could cause unnecessary panic or life changes.
• ICU Bed Allocation: If you only have 5 beds, you’d want to use them for patients who are most certainly critical.
• Drug Discovery Pre-Screening: Select molecules that are most likely to work, even if some potential candidates are missed.

(2) High Recall, Lower Precision #

✅ When to Use: #

• When missing a real case is dangerous
• When early detection can improve outcomes
• When follow-up tests or actions are safe and cheap

📌 Justification: #

• It’s better to catch every possible case, even if you have some false alarms.
• Especially important in serious or rapidly progressing conditions.

💡 Examples: #

• Cancer Screening: Better to flag more patients for follow-up than miss someone with early-stage cancer.
• Sepsis Prediction in ER: Alerting the care team early—even with some false alarms—can save lives.
• COVID-19 Testing in High-Risk Areas: Broad detection to prevent spread, even if some healthy people test positive.

🧠 Summary Table #