Machine Learning for Healthcare

Apply machine learning to healthcare using R programming

Welcome to this introduction to the University of Aberdeen’s 16‑week course, Machine Learning for Healthcare, which runs in January. Should you wish to continue to the full course, you can enjoy 10% off your registration using the code FutureLearn26.

How can machine learning improve healthcare decision-making and patient outcomes? As health data grows increasingly complex, applying algorithms to sensitive medical information requires specialised skills and ethical awareness.

This three week course equips you with practical machine learning skills tailored to healthcare. Using R and RStudio, you’ll build, evaluate, and deploy models that address clinical problems from disease prediction to treatment optimisation.

Understand machine learning foundations for healthcare

Explore core concepts including supervised, unsupervised, and reinforcement learning through healthcare examples like predictive diagnostics and pattern recognition.

Set up R and RStudio for health data analysis

Develop hands-on skills with R programming, RStudio, packages, and R Markdown for reproducible workflows. You’ll practice data wrangling, visualization, and creating shareable outputs.

Through practical exercises, you’ll prepare health datasets, explore patterns, and implement machine learning pipelines while maintaining ethical data handling standards.

Apply machine learning to real healthcare challenges

Learn to select appropriate algorithms for real-world problems, evaluating performance and addressing biases. You’ll use health data to build models, test generalisability, and interpret results for clinical use.

You’ll tackle challenges including ethical AI implementation, regulatory considerations, and building trustworthy systems for healthcare settings.

This course is ideal if you have:

• a background in healthcare or health research and want to learn more about machine learning, or
• a background in computational or data-intensive sciences and you’re keen to work in the health sector.

This course is ideal if you have:

• a background in healthcare or health research and want to learn more about machine learning, or
• a background in computational or data-intensive sciences and you’re keen to work in the health sector.

• Explain fundamental machine learning principles and algorithms used in healthcare, including supervised, unsupervised, and reinforcement learning, and differentiate these approaches from traditional statistical methods.
• Apply R and RStudio to prepare, manage, and visualise healthcare datasets using reproducible workflows while demonstrating ethical data handling practices.
• Analyse and evaluate machine learning models for healthcare applications by selecting, tuning, and testing algorithms, and assessing performance, bias, and generalisability.
• Interpret and communicate machine learning findings to support healthcare decision-making, considering clinical relevance, ethical implications, and regulatory requirements.