ArticleBMC public health2025
Identifying determinants of unmet healthcare needs in South Korea: a comparison of machine learning approaches for precision public health.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Buffering the healthcare gap: the role of Korean medicine in unmet healthcare needs among care recipients.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundDespite a comprehensive national health insurance system in South Korea, achieving universal healthcare access remains challenging. Unmet healthcare needs reflect a confluence of individual-level barriers and broader structural constraints. This study aimed to identify and compare the most influential factors determining unmet healthcare needs using multiple machine learning techniques.
methodsThis study included 1,489,560 adults aged ≥ 20 who participated in the Community Health Survey between 2016 and 2022. The variables were classified into predisposing, enabling, and need factors according to Andersen’s behavioral model. Five machine learning algorithms—Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost— were used to predict unmet healthcare needs. The model performance was evaluated using accuracy, precision, recall, and F1-score metrics alongside confusion matrices, with special emphasis on minimizing false negatives.
resultsLightGBM exhibited the highest accuracy (0.9222) with superior recall (0.7802) and F1-score (0.7772), demonstrating optimal performance in identifying individuals with unmet healthcare needs. Age, household income, subjective health status, frequency of alcohol consumption, education level, employment status, and walking frequency were found to be the most influential predictors of unmet healthcare needs.
conclusionsThe findings provide data-driven insights that enable policymakers to develop tailored interventions addressing the needs of vulnerable populations. This machine learning framework facilitates precision public health by reducing unmet healthcare needs, enhancing healthcare equity, and optimizing healthcare resource allocation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.