Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand.

ABSTRACT:

Background

In the age of big data, linked social and administrative health data in combination with machine learning (ML) is being increasingly used to improve prediction in chronic disease, e.g., cardiovascular diseases (CVD). In this study we aimed to apply ML methods on extensive national-level health and social administrative datasets to assess the utility of these for predicting future diabetes complications, including by ethnicity.

Methods

Five ML models were used to predict CVD events among all people with known diabetes in the population of New Zealand, utilizing nationwide individual-level administrative data.

Results

The Xgboost ML model had the best predictive power for predicting CVD events three years into the future among the population with diabetes (N 

SUBMITTER: Nghiem N 

PROVIDER: S-EPMC11438423 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature