Machine Learning for Risk Prediction of Infectious Diseases: A Scoping Review of Application, Predictive Performance, and Reporting Gaps

Background: Infectious diseases remain a major cause of global morbidity and mortality, with machine learning (ML) emerging as a promising approach for risk prediction. However, the heterogeneity of ML applications across diseases, algorithms, and validation practices impedes evidence synthesis and identification of best practices. Objective: This scoping review aims to systematically map the application of ML algorithms for infectious disease risk prediction, describe disease targets, study populations, data sources, performance metrics, and identify methodological strengths, limitations, and research gaps. Methods: We conducted a scoping review following JBI methodology and PRISMA-ScR guidelines. Four databases (PubMed, Scopus, Science Direct, IEEE Xplore) were searched from inception to May 2026. Studies will be included if they (a) human populations of any age, sex, or risk group; (b) use of at least one ML algorithm to build a risk prediction model for infectious disease incidences, not for diagnosis of existing infection or prognosis of already infected patients; (c) infectious diseases capable of human to human or zoonotic transmission or vector-borne diseases; (d) original research with an observational analytical design (cross sectional, case control, or cohort); and (e) reporting of at least one discrimination metric (sensitivity, specificity, area under the receiver operating characteristic curve [AUC]), calibration metric, or overall accuracy. Two reviewers will independently screen titles/abstracts and full texts. Data will be charted on a piloted form. Results will be synthesised narratively.