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Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets
journal contributionposted on 2023-11-09, 02:41 authored by N Nghiem, J Atkinson, Binh NguyenBinh Nguyen, A Tran-Duy, N Wilson
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-nearest neighbourhood (KNN) and random forest) with the traditional regression models. Results: The machine learning models had far better accuracy in predicting high health-cost users compared with the logistic models. The harmony score F1 (combining sensitivity and positive predictive value) of the machine learning models ranged from 30.6% to 41.2% (compared with 8.6–9.1% for the logistic models). Previous health costs, income, age, chronic health conditions, deprivation, and receiving a social security benefit were among the most important predictors of the CVD high-cost users. Conclusions: This study provides additional evidence that machine learning can be used as a tool together with big data in health economics for identification of new risk factors and prediction of high-cost users with CVD. As such, machine learning may potentially assist with health services planning and preventive measures to improve population health while potentially saving healthcare costs.
Preferred citationNghiem, N., Atkinson, J., Nguyen, B. P., Tran-Duy, A. & Wilson, N. (2023). Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets. Health Economics Review, 13(1), 9-. https://doi.org/10.1186/s13561-023-00422-1
Journal titleHealth Economics Review
PublisherSpringer Science and Business Media LLC
Online publication date2023-02-04
Machine learningHigh-cost usersCVD cost predictionHealth and social administrative dataNew Zealand4206 Public Health42 Health SciencesBasic Behavioral and Social ScienceCardiovascularBehavioral and Social ScienceHealth ServicesPreventionClinical ResearchHeart DiseaseGeneric health relevance3 Good Health and Well Being4203 Health services and systems3801 Applied economics