Abstract
Cardiovascular disease is responsible for an alarming number of deaths worldwide. Nowadays more people die of cardiovascular disease than any other type of disease. A considerable number of these deaths is caused by preventable risk factors. Thus, it is necessary to invest in cardiovascular disease prevention and take urgent action to reverse this scenario, reducing risky behaviours. The availability of a tool capable of regularly monitoring cardiac well-being indexes can work as an important means for sensitizing the population and preventing the appearance of preventable risk factors. This paper presents and discusses the implementation of a semi-automatic system capable of returning cardiac well-being indexes. The system allows for evaluating individual indexes of each user over time, considering the influence of past values, as well as for observing global statistics, which can be useful for public health decisions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mahmood, S.S., Levy, D., Vasan, R.S., Wang, T.J.: The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet 383, 999–1008 (2014)
Kannel, W.B., Dawber, T.R., Kagan, A., Revotskie, N., Stokes, J.: Factors of risk in the development of coronary heart disease - six-year follow-up experience. The Framingham study. Ann. Intern. Med. 55, 33–50 (1961)
Andersson, C., Johnson, A.D., Benjamin, E.J., Levy, D., Vasan, R.S.: 70-Year legacy of the Framingham heart study. Nat. Rev. Cardiol. 1968 (2019)
Wilson, P.W.F., D’Agostino, R.B., Levy, D., Belanger, A.M., Silbershatz, H., Kannel, W.B.: Prediction of coronary heart disease using risk factor categories. Circulation 97, 1837–1847 (1998)
Chen, H., Chiang, R.H.L., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36, 1165–1188 (2012)
QRISK3. https://www.qrisk.org/three/. Accessed 13 Mar 2020
Instituto Superiore di Sanità : Il Progetto Cuore. https://www.cuore.iss.it/valutazione/calc-rischio. Accessed 13 Mar 2020
Srinivas, K., Rao, G.R., Govardhan, A.: Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques. In: 5th International on Computer Science and Education, pp. 1344–1349 (2010)
Kim, J.K., Kang, S.: Neural network-based coronary heart disease risk prediction using feature correlation analysis. J. Healthc. Eng. 2017, 13 (2017)
Badgeley, M.A., Shameer, K., Glicksberg, B.S., Tomlinson, M.S., Levin, M.A., McCormick, P.J., Kasarskis, A., Reich, D.L., Dudley, J.T.: EHDViz: clinical dashboard development using open-source technologies. BMJ Open 6(3), 1–11 (2016)
Cardiovascular Disease Dataset. https://www.kaggle.com/sulianova/cardiovascular-disease-dataset. Accessed 05 Feb 2020
Hitachi Vantara. https://www.hitachivantara.com/en-us/home.html. Accessed 05 Feb 2020
Weka. https://www.cs.waikato.ac.nz/ml/weka/. Accessed 05 Feb 2020
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, Burlington (2017)
Santos, V., Belo, O.: No need to type slowly changing dimensions. In: Proceedings of the IADIS International Conference Information Systems, pp. 129–136 (2011)
Power BI. https://powerbi.microsoft.com/pt-pt/. Accessed 05 Feb 2020
Acknowledgement
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Duarte, A., Belo, O. (2021). Preventing Cardiovascular Disease Development Establishing Cardiac Well-Being Indexes. In: Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020). PACBB 2020. Advances in Intelligent Systems and Computing, vol 1240. Springer, Cham. https://doi.org/10.1007/978-3-030-54568-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-54568-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-54567-3
Online ISBN: 978-3-030-54568-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)