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Cardiovascular Risk Detection Through Big Data Analysis

Cardiovascular Risk Detection Through Big Data Analysis

Miguel A. Sánchez-Acevedo, Zaydi Anaí Acosta-Chi, Ma. del Rocío Morales-Salgado
Copyright: © 2020 |Volume: 5 |Issue: 2 |Pages: 11
ISSN: 2379-738X|EISSN: 2379-7371|EISBN13: 9781799808381|DOI: 10.4018/IJBDAH.2020070101
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MLA

Sánchez-Acevedo, Miguel A., et al. "Cardiovascular Risk Detection Through Big Data Analysis." IJBDAH vol.5, no.2 2020: pp.1-11. http://doi.org/10.4018/IJBDAH.2020070101

APA

Sánchez-Acevedo, M. A., Acosta-Chi, Z. A., & Morales-Salgado, M. D. (2020). Cardiovascular Risk Detection Through Big Data Analysis. International Journal of Big Data and Analytics in Healthcare (IJBDAH), 5(2), 1-11. http://doi.org/10.4018/IJBDAH.2020070101

Chicago

Sánchez-Acevedo, Miguel A., Zaydi Anaí Acosta-Chi, and Ma. del Rocío Morales-Salgado. "Cardiovascular Risk Detection Through Big Data Analysis," International Journal of Big Data and Analytics in Healthcare (IJBDAH) 5, no.2: 1-11. http://doi.org/10.4018/IJBDAH.2020070101

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Abstract

Cardiovascular diseases are the main cause of mortality in the world. As more people suffer from diabetes and hypertension, the risk of cardiovascular disease (CVD) increases. A sedentary lifestyle, an unhealthy diet, and stressful activities are behaviors that can be changed to prevent CVD. Taking measures to prevent CVD lowers the cost of treatments and reduces mortality. Data-driven plans generate more effective results and can be applied to groups with similar characteristics. Currently, there are several databases that can be used to extract information in real time and improve decision making. This article proposes a methodology for the detection of CVD and a web tool to analyze the data more effectively. The methodology for extracting, describing, and visualizing data from a state-level case study of CVD in Mexico is presented. The data is obtained from the databases of the National Institute of Statistics and Geography (INEGI) and the National Survey of Health and Nutrition (ENSANUT). A k-nearest neighbor (KNN) algorithm is proposed to predict missing data.