ArticleGlobal health research and policy2022
Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS).
Article in Global health research and policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 11 citations in OpenAlex.
- A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Trends and predictive research on the global burden of ischemic heart disease from 1990 to 2021: an analysis of the Global Burden of Disease study 2021.Frontiers in public health · 2025Article
- Prevalence of Musculoskeletal Disorders in Heavy Vehicle Drivers and Office Workers: A Comparative Analysis Using a Machine Learning Approach.Healthcare (Basel, Switzerland) · 2024Article
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
- Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires.Scientific reports · 2024Article
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16 authors at 4 institutions in 1 country.
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Abstract
backgroundIdentifying factors associated with cardiovascular disease (CVD) is critical for its prevention, but this topic is scarcely investigated in Kashgar prefecture, Xinjiang, northwestern China. We thus explored the CVD epidemiology and identified prominent factors associated with CVD in this region.
methodsA total of 1,887,710 adults at baseline (in 2017) of the Kashgar Prospective Cohort Study were included in the analysis. Sixteen candidate factors, including seven demographic factors, 4 lifestyle factors, and 5 clinical factors, were collected from a questionnaire and health examination records. CVD was defined according to International Clinical Diagnosis (ICD-10) codes. We first used logistic regression models to investigate the association between each of the candidate factors and CVD. Then, we employed 3 machine learning methods-Random Forest, Random Ferns, and Extreme Gradient Boosting-to rank and identify prominent factors associated with CVD. Stratification analyses by sex, ethnicity, education level, economic status, and residential setting were also performed to test the consistency of the ranking.
resultsThe prevalence of CVD in Kashgar prefecture was 8.1%. All the 16 candidate factors were confirmed to be significantly associated with CVD (odds ratios ranged from 1.03 to 2.99, all p values < 0.05) in logistic regression models. Further machine learning-based analysis suggested that age, occupation, hypertension, exercise frequency, and dietary pattern were the five most prominent factors associated with CVD. The ranking of relative importance for prominent factors in stratification analyses showed that the factor importance generally followed the same pattern as that in the overall sample.
conclusionsCVD is a major public health concern in Kashgar prefecture. Age, occupation, hypertension, exercise frequency, and dietary pattern might be the prominent factors associated with CVD in this region.In the future, these factors should be given priority in preventing CVD in future.
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