Artificial intelligence and prediction of cardiometabolic disease: Systematic review of model performance and potential benefits in indigenous populations

Abstract

Background: Indigenous peoples often have higher rates of morbidity and mortality associated with cardiometabolic disease (CMD) than non-Indigenous people and this may be even more so in urban areas. The use of electronic health records and expansion of computing power has led to mainstream use of artificial intelligence (AI) to predict the onset of disease in primary health care (PHC) settings. However, it is unknown if AI and in particular machine learning is used for risk prediction of CMD in Indigenous peoples.

Methods: We searched peer-reviewed literature using terms associated with AI machine learning, PHC, CMD, and Indigenous peoples.

Results: We identified 13 suitable studies for inclusion in this review. Median total number of participants was 19,270 (range 911-2,994,837). The most common algorithms used in machine learning in this setting were support vector machine, random forest, and decision tree learning. Twelve studies used the area under the receiver operating characteristic curve (AUC) to measure performance. Two studies reported an AUC of >0.9. Six studies had an AUC score between 0.9 and 0.8, 4 studies had an AUC score between 0.8 and 0.7. 1 study reported an AUC score between 0.7 and 0.6. Risk of bias was observed in 10 (77 %) studies.

Conclusion: AI machine learning and risk prediction models show moderate to excellent discriminatory ability over traditional statistical models in predicting CMD. This technology could help address the needs of urban Indigenous peoples by predicting CMD early and more rapidly than conventional methods.

Keywords: Artificial intelligence; Cardiometabolic disease; Indigenous Australians; Indigenous peoples; Machine learning; Primary health care; Risk prediction.