ArticleScientific reports2022
Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Machine learning prediction of survival in centenarians after age 100: a retrospective, population-based cohort study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- A risk prediction model for venous thromboembolism in hospitalized patients with thoracic trauma: a machine learning, national multicenter retrospective study.World journal of emergency surgery : WJES · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection and treatment of diseases through health checkups are effective in improving life expectancy. In this study, we compared the predictive ability for 5-year mortality between two machine learning-based models (gradient boosting decision tree [XGBoost] and neural network) and a conventional logistic regression model in 116,749 health checkup participants. We built prediction models using a training dataset consisting of 85,361 participants in 2008 and evaluated the models using a test dataset consisting of 31,388 participants from 2009 to 2014. The predictive ability was evaluated by the values of the area under the receiver operating characteristic curve (AUC) in the test dataset. The AUC values were 0.811 for XGBoost, 0.774 for neural network, and 0.772 for logistic regression models, indicating that the predictive ability of XGBoost was the highest. The importance rating of each explanatory variable was evaluated using the SHapley Additive exPlanations (SHAP) values, which were similar among these models. This study showed that the machine learning-based model has a higher predictive ability than the conventional logistic regression model and may be useful for risk assessment and health guidance for health checkup participants.
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.