Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance

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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“…Its aim is to help the personalization of medical practice, so that it can be tailored for the patients [ 28 ]. In sport medicine, ML can be used for both performance and risk prediction [ 29 , 30 ]. Further, ML can identify molecular markers for cancer treatments, evaluate postoperative surgical outcomes, and supply an automated interpretation of an ECG or an automated detection of a lung nodule in a chest X-ray [ 31 ].…”

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“…For example, the study reported in [ 25 ] showed how AI is useful for determining cardiovascular risk in athletes through data mining of distributed databases .…”

“…The three illustrated potentials [ 25 , 26 , 27 ] are also important in DP. In fact, in DP, the need for categorizing images merges with the need to make decisions and/or deduce approaches through actions on large databases and data sets or with other needs not based on medical images [ 1 , 19 , 20 ].…”

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“…Further research should also provide evidence for the effectiveness and clinical relevance of hand strength testing in the assessment and prediction of critical health conditions. Barbieri et al [9] investigated the efficacy and accuracy of a data mining methodology in predicting cardiovascular risk based on anthropometric, demographic, and biomedical data from a very large sample of the population involved in competitive sports practice. The procedure was conducted using a decision tree and logistic regression to classify individuals as at-risk or not.…”