Learning Outcomes

By the end of this workshop, participants will be able to:

1. Describe the fundamental concepts of machine learning and distinguish them from traditional statistical approaches used in health research.
2. Prepare and explore real-world clinical or public health datasets for machine learning analysis, including handling missing data and selecting relevant predictors.
3. Apply common supervised machine learning techniques (e.g., logistic regression, decision trees, and random forests) to healthcare data using R/Python.
4. Evaluate and compare machine learning models using appropriate performance metrics such as accuracy, sensitivity, specificity, confusion matrices, and ROC curves.
5. Interpret machine learning outputs in clinically and epidemiologically meaningful ways to support evidence-based decision-making.
6. Recognise key ethical, methodological, and practical considerations in applying machine learning within healthcare and public health settings, including bias, fairness, and explainability.

Ticket prices

• City St George’s staff and students - £10.00
• External participants - £50.00