Machine Learning Models in Prediabetes Screening: A Systematic Review

Abstract

Introduction: The increasing prevalence of type 2 Diabetes Mellitus (DM) can be done from identifying those with prediabetes and offer early interventions by utilising prescreening diagnostic tools. Machine learning algorithms and big data mining approaches have been postulated for predictive disease modelling in hospital and clinical settings. Aim: To outline the relative performance accuracies in predicting prediabetes conditions in different machine learning algorithms. Materials and Methods: A systematic literature search was conducted at Universiti of Kebangsaan, Kuala Lumpur, Malaysia, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review protocol, and the research question was formulated based on the keywords of “Prediabetes” (Population), “Internet of Things” and “prediction model” (Intervention) and “screening” and “risk” (Outcome). International Prospective Register of Systematic Reviews (PROSPERO) registration (CRD42021264947) was done and databases were screened on 10th-24th June 2021 via Web of Science, Scopus, PubMed, Ovid and EBSCOhost. Inclusion criteria was English language prediction studies published between 2011-2021. Review articles, editorials, proceedings, commentary articles and articles not focusing on prediabetes were excluded. The quality of the articles was ranked via the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results: A total of five articles that were published in 2014-2021 were included. The sample sizes ranged from 570 to 24,331 participants. Three studies (South Korea, United State of America (USA), Japan) suggested the applicability of the screening score prediction models for use in clinical settings related to personalised risk assessment and targeted interventions, with the predictors used being suitable for either the clinic or hospital. The simplicity of gender, age, Body Mass Index (BMI), blood pressure and waist circumference as predictors suggested that they can be utilised by the community. Conclusion: This review highlights the fact that the heterogeneity of the population used and validation issues may affect generalisation. Future studies should address these concerns to guide advocacy among healthcare providers in clinical practice as well as in data and expertise sharing for developing and validating urgently needed prediabetic prediction models.

Keywords

• artificial intelligence
• detection
• impaired glucose tolerance
• internet of things
• predevelopment diabetes
• prediction model
• risk