ArticleInternational journal of environmental research and public health2020
Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 18 citations in OpenAlex.
- Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety-A Narrative Review.Sports (Basel, Switzerland) · 2024Review
- The Athlete's Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research.Journal of cardiovascular development and disease · 2022Review
- Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods.BMC medical informatics and decision making · 2022Article
- Artificial Intelligence in Digital Pathology: What Is the Future?Healthcare (Basel, Switzerland) · 2021Article
- Physical Activity for Health and Wellness.International journal of environmental research and public health · 2021Article
- How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic.International journal of environmental research and public health · 2021Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine sport medical examinations, which included electrocardiography at rest. Subjects were involved in competitive sport practice, for which medical clearance was needed. Outcomes were negative for the largest majority, as expected in an active population. Resampling was applied to balance positive/negative class ratio. A decision tree and logistic regression were used to classify individuals as either at risk or not. The receiver operating characteristic curve was used to assess classification performances. Data mining and resampling improved cardiovascular risk assessment in terms of increased area under the curve. The proposed methodology can be effectively applied to biomedical data in order to optimize clinical decision making, and-at the same time-minimize the amount of unnecessary examinations.
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Registered trials
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