ArticleScientific reports2021
Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
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- Identifying direct risk factors in UK Biobank via simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Spatial inequalities and contextual correlates of methylphenidate dispensing in Jiangsu Province, China.Frontiers in pharmacology · 2026Article
- Integrative Machine Learning and Bayesian Analysis Reveals Atrial Fibrillation as a Key Predictor of Severe COVID-19 Outcomes.Clinical and translational science · 2025Article
- Interpretability of automated machine learning methods in psychological research: A tutorial with AutoGluon in Python.Behavior research methods · 2025Article
- Decoding the adolescent non-suicidal self-injury: understanding with interpretable machine learning insights.BMC public health · 2025Article
- Factors associated with admission to elderly medical-welfare facilities in South Korea: a cross-sectional machine-learning study.BMJ open · 2025Article
- Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data.International journal of clinical pharmacy · 2025Article
- An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications.Perspectives of the ASHA special interest groups · 2025Article
- Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning.Scientific reports · 2025Article
- Article
- Regression and machine learning approaches identify potential risk factors for glioblastoma multiforme.Brain communications · 2025Article
- Machine learning prediction of depression in culturally diverse families: Findings from the Korea Community Health Survey.Frontiers in public health · 2025Article
- Assessing regional competitiveness in Peru: An approach using nonlinear machine learning models.PloS one · 2025Article
- Assessing Hwa-byung Vulnerability Using the Hwa-byung Personality Scale: a comparative study of machine learning approaches.Journal of pharmacopuncture · 2024Article
- Impact of sleep duration and dietary patterns on risk of metabolic syndrome in middle-aged and elderly adults: a cross-sectional study from a survey in Anhui, Eastern China.Lipids in health and disease · 2024Article
- LLpowershap: logistic loss-based automated Shapley values feature selection method.BMC medical research methodology · 2024Article
- Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis.JACC. Advances · 2024Article
- Article
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4 authors.
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Abstract
We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using a Shapley values-based feature attribution method, SHAP values. Cox models controlled for false discovery rate were used for confounder adjustment, interpretability, and further validation. The pipeline was tested using information from 502,506 UK Biobank participants, aged 37-73 years at recruitment and followed over seven years for mortality registrations. From the 11,639 predictors included in GBDT, 193 potential risk factors had SHAP values ≥ 0.05, passed the correlation test, and were selected for further modelling. Of the total variable importance summed up, 60% was directly health related, and baseline characteristics, sociodemographics, and lifestyle factors each contributed about 10%. Cox models adjusted for baseline characteristics, showed evidence for an association with mortality for 166 out of the 193 predictors. These included mostly well-known risk factors (e.g., age, sex, ethnicity, education, material deprivation, smoking, physical activity, self-rated health, BMI, and many disease outcomes). For 19 predictors we saw evidence for an association in the unadjusted but not adjusted analyses, suggesting bias by confounding. Our GBDT-SHAP pipeline was able to identify relevant predictors 'hidden' within thousands of variables, providing an efficient and pragmatic solution for the first stage of hypothesis free risk factor identification.
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