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Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance

Author

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  • Davide Barbieri

    (Department of Biomedical and Specialty Surgical Sciences, Faculty of Medicine, Pharmacy and Prevention, University of Ferrara, 44121 Ferrara, Italy
    From 1 November 2020 the name of Department will be Neuroscience and Rehabilitation.)

  • Nitesh Chawla

    (Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN 46556, USA)

  • Luciana Zaccagni

    (Department of Biomedical and Specialty Surgical Sciences, Faculty of Medicine, Pharmacy and Prevention, University of Ferrara, 44121 Ferrara, Italy
    Biomedical Sport Studies Center, University of Ferrara, 44123 Ferrara, Italy)

  • Tonći Grgurinović

    (Polyclinic for Occupational Health and Sports of Zagreb Sports Association with Laboratory of Medical Biochemistry, 10000 Zagreb, Croatia)

  • Jelena Šarac

    (Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia)

  • Miran Čoklo

    (Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia)

  • Saša Missoni

    (Institute for Anthropological Research, 10000 Zagreb, Croatia
    School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia)

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.

Suggested Citation

  • Davide Barbieri & Nitesh Chawla & Luciana Zaccagni & Tonći Grgurinović & Jelena Šarac & Miran Čoklo & Saša Missoni, 2020. "Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance," IJERPH, MDPI, vol. 17(21), pages 1-9, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:21:p:7923-:d:436380
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    References listed on IDEAS

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    5. Azra Ramezankhani & Omid Pournik & Jamal Shahrabi & Fereidoun Azizi & Farzad Hadaegh & Davood Khalili, 2016. "The Impact of Oversampling with SMOTE on the Performance of 3 Classifiers in Prediction of Type 2 Diabetes," Medical Decision Making, , vol. 36(1), pages 137-144, January.
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    Cited by:

    1. Emanuela Gualdi-Russo & Luciana Zaccagni, 2021. "Physical Activity for Health and Wellness," IJERPH, MDPI, vol. 18(15), pages 1-6, July.
    2. Davide Barbieri & Enrico Giuliani & Anna Del Prete & Amanda Losi & Matteo Villani & Alberto Barbieri, 2021. "How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(14), pages 1-10, July.

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