Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.

ABSTRACT:

Background

The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.

Methods

PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were

SUBMITTER: Adeoye J 

PROVIDER: S-EPMC9751812 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature