Machine Learning approaches for Predicting Medical Costs in Oncology Patients: A Systematic Literature Review

Abstract

Although the use of machine learning (ML) in healthcare has increased significantly, a critical systematization of its application to medical cost prediction is still lacking. This paper aims to rigorously examine recent literature to identify methodological approaches, knowledge gaps, and emerging trends related to the economic use of ML in health. To this end, a systematic review of 71 papers was conducted, complemented by bibliometric analysis, journal quartile assessment, and thematic categorization. These strategies were applied across highly recognized academic databases, including Scopus, IEEE Xplore, ACM Digital Library, PubMed, and Springer Nature Link. The main findings indicate that: (1) most studies are concentrated in highly digitalized countries, which restricts their applicability in less developed contexts; (2) although a significant number of publications appear in Q1 journals, they do not always achieve high levels of scientific objectivity; and (3) the predominant topics focus on image-based diagnosis, while the prediction of medical costs remains an emerging and underexplored field. Overall, the results highlight a substantial gap between the technical development of ML and its integration into financial decision-making in healthcare. It is recommended to promote research with greater geographical diversity, grounded in more robust theoretical frameworks and guided by ethical principles that ensure implementation.