There are a host of applications for machine learning in epidemiological and medical research such as predicting disease risk, analysing large datasets and creating personalised treatment plans for precision medicine. However, many resources that teach machine learning techniques are highly technical and designed for those with advanced training in the mathematical sciences. This is a practical guide aimed at epidemiologists and medical researchers, written with a clear, non-mathematical approach. This book covers different methods for assessing model performance and quality and how to integrate machine learning into research. This is followed by step-by-step guidance for different techniques with real life examples so that readers can directly apply it to their work. Discussions of output, interpreting findings and implications for practice are also included, equipping researchers to draw reliable, meaningful conclusions from complex data.
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