Abstract
Cervical cancer represents a significant public health challenge, particularly affecting women's health globally. This study aims to advance the understanding of cervical cancer risk prediction research through a bibliometric analysis. The study identified 800 records from Scopus and Web of Science databases, which were reduced to 142 unique records after removing duplicates. Out of 100 abstracts assessed, 42 were excluded based on specific criteria, resulting in 58 studies included in the bibliometric review. Multiple scoping methods such as thematic analysis, citation analysis, bibliographic coupling, natural language processing, Latent Dirichlet Allocation and other visualisation techniques were used to analyse related publications between 2013 and 2024. The key findings revealed the importance of interdisciplinary collaboration in cervical cancer risk prediction, integrating expertise from mathematical disciplines, biomedical health, healthcare practitioners, public health, and policy. This approach significantly enhanced the accuracy and efficiency of cervical cancer detection and predictive modelling by adopting advanced machine learning algorithms, such as random forests and support vector machines. The main challenges were the lack of external validation on independent datasets and the need to address model interpretability to ensure healthcare providers understand and trust the predictive models. The study revealed the importance of interdisciplinary collaboration in cervical cancer risk prediction. It made recommendations for future research to focus on increasing the external validation of models, improving model interpretability, and promoting global research collaborations to enhance the comprehensiveness and applicability of cervical cancer risk prediction models.