Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.

ABSTRACT:

Objectives

To optimise planning of public health services, the impact of high-cost users needs to be considered. However, most of the existing statistical models for costs do not include many clinical and social variables from administrative data that are associated with elevated health care resource use, and are increasingly available. This study aimed to use machine learning approaches and big data to predict high-cost users among people with cardiovascular disease (CVD).

Methods

We used nationally representative linked datasets in New Zealand to predict CVD prevalent cases with the most expensive cost belonging to the top quintiles by cost. We compared the performance of four popular machine learning models (L1-regularised logistic regression, classification trees, k-near

SUBMITTER: Nghiem N 

PROVIDER: S-EPMC9898915 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature