Machine learning-based dietary pattern analysis: a narrative review and future perspectives

Over recent decades, nutritional epidemiology has evolved from nutrient-specific investigations to dietary pattern analysis, complementing traditional dietary assessment methods. With the global aging population and rising chronic disease burden, particularly cancer, identifying modifiable dietary risk factors through holistic dietary assessments is essential. Data-driven dietary pattern analysis methods, including principal component analysis and cluster analysis, have been widely used to derive dietary patterns. However, challenges in cluster quality persist, especially with dietary data. Recent advances in machine learning, including non-linear dimensionality reduction, offer promising strategies to enhance cluster separability in dietary pattern research. This review summarises the evolution of dietary assessment methodologies, outlines the development of dietary pattern analytical approaches and synthesises epidemiological evidence linking clustering-driven dietary patterns to cancer and metabolic disease risk. Furthermore, it highlights methodological gaps and discusses future directions emphasising the integration of machine learning methods for practical application in personalised cancer prevention. Establishing standardised frameworks combining dimensionality reduction and clustering may facilitate the translation of validated dietary patterns into digital platforms for widespread public health impact.