A review of machine learning approaches for diabetes risk prediction

Diabetes risk is increasingly predicted using machine learning, but how these models are built and evaluated will determine whether they truly help patients. This review begins by revisiting traditional risk scores based on logistic regression, which use a small set of routine variables such as age, BMI and simple blood tests. These conventional tools are practical and easy to interpret, yet they often miss complex patterns and differences between populations, and may therefore underestimate or overestimate risk in specific groups. The paper synthesises recent work on a wide range of machine learning approaches, from random forests and gradient boosting to neural networks and time-series models, using data from electronic health records, surveys, hospital cohorts and wearable devices. It shows how adding lifestyle factors such as physical activity, diet, sleep and smoking to standard clinical variables can improve prediction and, importantly, draw attention to behaviours that can be changed. The review also describes, in practical terms, how modern feature selection and optimisation techniques—such as genetic algorithms and related metaheuristic methods—can reduce a large set of variables to a smaller group of genuinely informative predictors, so that the resulting models tend to be not only more accurate but also easier for clinicians and researchers to interpret. Beyond headline performance, the article places strong emphasis on sound validation and transparent reporting, encouraging routine use of cross-validation, independent test cohorts and a broader panel of metrics rather than relying on accuracy alone. It also underscores the growing importance of explainability methods such as SHAP values, which clarify which inputs are most responsible for a given risk estimate, and calls for systematic checks of model performance across demographic subgroups to avoid deepening existing health inequalities. Taken together, the review suggests that machine learning can genuinely enhance conventional diabetes risk tools, but only if fairness, interpretability and real-world implementation are built into the design from the outset rather than added as an afterthought.