Practical guide to building machine learning-based clinical prediction models using imbalanced datasets.

Area of Science:

• Medical Informatics
• Machine Learning
• Surgical Research

Background:

• Clinical prediction models are crucial for identifying rare, high-risk events.
• Developing these models is challenging due to imbalanced datasets.

Discussion:

• This guide provides foundational principles for prediction model development, focusing on imbalanced data.
• Key areas covered include feature engineering, algorithm selection, and model evaluation specific to rare events.
• A clinical example with code illustrates practical considerations and potential pitfalls.

Key Insights:

• Understanding imbalanced datasets is critical for robust clinical prediction models.
• Specific design and evaluation techniques are necessary for rare event prediction.
• Practical application through code examples enhances comprehension for surgeons and data scientists.

Outlook:

• Facilitates the development of more accurate and reliable clinical prediction models in surgery.
• Empowers the surgical community to critically appraise existing prediction models.
• Aims to improve patient outcomes through enhanced predictive capabilities.