Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance

Davide Barbieri, Nitesh Chawla, Luciana Zaccagni, Tonći Grgurinović, Jelena Šarac, Miran Čoklo and Saša Missoni
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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
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
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

IJERPH, 2020, vol. 17, issue 21, 1-9

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.

Keywords: medical diagnostic; decision tree; logistic regression; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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