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Revista chilena de cardiología

versión On-line ISSN 0718-8560

Resumen

CARDENAS, Claudio et al. Design of a predictive model of cardiovascular screening using decision trees: propensity of patients to present type 2 diabetes, arterial hypertension or dyslipidemia. Pilot study commune of Quellón, Chiloé. Rev Chil Cardiol [online]. 2018, vol.37, n.2, pp.126-133. ISSN 0718-8560.  http://dx.doi.org/10.4067/S0718-85602018000200126.

Introduction

: Data Mining is increasingly popular in the health field because there is a need for an efficient analytical methodology to detect unknown and valuable information of health data.

Objective

: To develop a predictive model using data mining techniques, specifically Decision Trees, to investigate patients with a propensity to develop Type II Diabetes, Arterial Hypertension or Dyslipidemia.

The data of adult patients presenting Type II diabetes, Hypertension or Dyslipidemia being followed in a preventive cardiovascular control program were analyzed with the aim of unveiling phenomena that could help develop the prediction of these risk factors.

Results

: With respect to other decision tree algorithms, Algorithm C 5, showed a greater predictive power. The variables age and waist circumference had the greatest power of discrimination for DM2, HTA or DLP. The C 5 algorithm reached a global precision of 83.01% in the test partition. Then, in the same partition the model managed to discriminate a patient with some of the risk factors in 85.25% of cases, and to rule out any of them in 80.27% of cases.

Conclusion

: Data Mining, specifically decisión tree models, is a valid alternative for early detection of cardiovascular of risk factors.

Palabras clave : cardiovascular research, datamining; decision trees; diabetes mellitus II; hypertension; dyslipidemia.

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