Skip to main content
SpringerLink
Log in

Application of a fuzzy unit hypercube in cardiovascular risk classification

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Most standard cardiovascular disease (CVD) risk assessment models are based on traditional quantitative approaches. Such models oversimplify complex interactions emanating from the imprecise nature of CVD risk factors. As such, approaches that can handle uncertainty due to imprecision need to be explored. This study proposes a cardiovascular risk classification model based on the geometry of fuzzy sets, which allows for a multidimensional display of qualitative properties associated with risk attributes—that are defined in a fuzzy sense. Within this structure, a risk concept (which defines the degree of risk severity) is simply a continuum of points of the hypercube. Consequently, an individual’s risk status would naturally be represented by an ordered fuzzy within the continuum. This representation forms an excellent comparative framework through measures of similarity where an individual’s relative position in the continuum can be given as degrees of compatibility with the underlying risk concepts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Acs A, Ludwig C, Bereza B, Einarson T, Panton U (2017) Economic burden of cardiovascular disease in type 2 diabetes: a systematic review. Value Health 20(9):A476–A477

    Article  Google Scholar 

  • Alqudah AM (2017) Fuzzy expert system for coronary heart disease diagnosis in Jordan. Health Technol 7(2–3):215–222

    Article  Google Scholar 

  • Anderson KM, Odell PM, Wilson PW, Kannel WB (1991) Cardiovascular disease risk profiles. Am Heart J 121(1):293–298

    Article  Google Scholar 

  • Anooj P (2012) Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules. J King Saud Univ Comput Inf Sci 24(1):27–40

    Google Scholar 

  • Barro S, Marín R (2013) Fuzzy logic in medicine, vol 83. Physica-Verlag, Heidelberg

    MATH  Google Scholar 

  • Berry JD, Dyer A, Cai X, Garside DB, Ning H, Thomas A, Greenland P, Van Horn L, Tracy RP, Lloyd-Jones DM (2012) Lifetime risks of cardiovascular disease. N Engl J Med 366(4):321–329

    Article  Google Scholar 

  • Boon N, Boyle R, Bradbury K, Buckley J, Connolly S, Craig S, Deanfield J, Doherty P, Feher M, Fox K et al (2014) Joint British Societies’ consensus recommendations for the prevention of cardiovascular disease (JBS3). Heart 100(Suppl 2):ii1–ii67

    Article  Google Scholar 

  • Buede DM (1994) Examination of the fuzzy subsethood theorem for data fusion. In: Multisensor fusion and integration for intelligent systems, 1994. IEEE international conference on MFI’94, IEEE, pp 430–434

  • Ephzibah E (2011) A hybrid genetic-fuzzy expert system for effective heart disease diagnosis. In: Wyld DC, Wozniak M, Chaki N, Meghanathan N, Nagamalai D (eds) Advances in computing and information technology. Springer, Berlin, pp 115–121

    Chapter  Google Scholar 

  • Go AS, Chertow GM, Fan D, McCulloch CE, Hsu Cy (2004) Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 351(13):1296–1305

    Article  Google Scholar 

  • Grossi E (2006) How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods. BMC Cardiovasc Disord 6(1):20

    Article  MathSciNet  Google Scholar 

  • Helgason CM, Jobe TH (1998) The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke. Neural Netw 11(3):549–555

    Article  Google Scholar 

  • Helgason CM, Watkins FA, Jobe TH (2002) Measurable differences between sequential and parallel diagnostic decision processes for determining stroke subtype: a representation of interacting pathologies. Thromb Haemost 88(02):210–212

    Google Scholar 

  • Kahtan H, Zamli KZ, Fatthi WNAWA, Abdullah A, Abdulleteef M, Kamarulzaman NS (2018) Heart disease diagnosis system using fuzzy logic. In: Proceedings of the 2018 7th international conference on software and computer applications, ACM, pp 297–301

  • Kasbe T, Pippal RS (2017) Design of heart disease diagnosis system using fuzzy logic. In: 2017 international conference on energy, communication, data analytics and soft computing (ICECDS), IEEE, pp 3183–3187

  • Kim J, Lee J, Lee Y (2015) Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree. Healthc Inform Res 21(3):167–174

    Article  Google Scholar 

  • Kosko B (1990) Fuzziness vs. probability. Int J General Syst 17(2–3):211–240

    Article  Google Scholar 

  • Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T (2017) Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol 69(21):2657–2664

    Article  Google Scholar 

  • Leal J, Luengo-Fernández R, Gray A, Petersen S, Rayner M (2006) Economic burden of cardiovascular diseases in the enlarged european union. Eur Heart J 27(13):1610–1619

    Article  Google Scholar 

  • Lewis M, Lawry J (2016) Hierarchical conceptual spaces for concept combination. Artif Intell 237:204–227

    Article  MathSciNet  Google Scholar 

  • Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Després JP, Fullerton HJ et al (2015) Heart disease and stroke statistics—2016 update: a report from the American heart association. Circulation 133(4):e38–e360. https://doi.org/10.1161/CIR.0000000000000350

    Article  Google Scholar 

  • Narayan KV, Ali MK, Koplan JP (2010) Global noncommunicable diseases—where worlds meet. N Engl J Med 363(13):1196–1198

    Article  Google Scholar 

  • Nieto JJ, Torres A (2003) Midpoints for fuzzy sets and their application in medicine. Artif Intell Med 27(1):81–101

    Article  Google Scholar 

  • O’Donnell CJ, Elosua R (2008) Cardiovascular risk factors. Insights from framingham heart study. Revista Espanola de Cardiologia (English Edition) 61(3):299–310

    Article  Google Scholar 

  • O’gara PT, Kushner FG, Ascheim DD, Casey DE, Chung MK, De Lemos JA, Ettinger SM, Fang JC, Fesmire FM, Franklin BA et al (2013) 2013 ACCF/AHA guideline for the management of st-elevation myocardial infarction: executive summary: a report of the American college of cardiology foundation/American heart association task force on practice guidelines. J Am Coll Cardiol 61(4):485–510

    Article  Google Scholar 

  • Rajeswari K, Vaithiyanathan V (2011) Heart disease diagnosis: an efficient decision support system based on fuzzy logic and genetic algorithm. Int J Decis Sci Risk Manag 3(1–2):81–97

    Google Scholar 

  • Rickard JT (2006) A concept geometry for conceptual spaces. Fuzzy Optim Decis Mak 5(4):311–329

    Article  MathSciNet  Google Scholar 

  • Ross TJ (2009) Fuzzy logic with engineering applications. Wiley, Hoboken

    Google Scholar 

  • Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, Ahmed M, Aksut B, Alam T, Alam K et al (2017) Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol 70(1):1–25

    Article  Google Scholar 

  • Sadegh-Zadeh K (1999) Fundamentals of clinical methodology: 3. Nosology. Artif Intell Med 17(1):87–108

    Article  Google Scholar 

  • Sadegh-Zadeh K et al (2012) Handbook of analytic philosophy of medicine. Springer, Dordrecht

    Book  Google Scholar 

  • Savinov AA (1999) Application of multi-dimensional fuzzy analysis to decision making. In: Roy R, Furuhashi T, Chawdhry PK (eds) Advances in soft computing. Springer, London, pp 301–314

    Chapter  Google Scholar 

  • Stamler J, Vaccaro O, Neaton JD, Wentworth D, Group MRFITR et al (1993) Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the multiple risk factor intervention trial. Diabetes Care 16(2):434–444

  • Ventola CL (2014) Mobile devices and apps for health care professionals: uses and benefits. Pharm Ther 39(5):356

    Google Scholar 

  • Vijaya K, Khanna Nehemiah H, Kannan A, Bhuvaneswari N (2010) Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases. Int J Data Min Model Manag 2(4):388–402

    Google Scholar 

  • Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97(18):1837–1847

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

  • Zahan S, Bogdan R, Capalneanu R (2000) Fuzzy expert system for cardiovascular disease diagnosis-tests and performance evaluation. In: Proceedings of the 5th seminar on neural network applications in electrical engineering, 2000. NEUREL 2000. IEEE, pp 65–68

  • Zhang XS, Leu FY, Yang CW, Lai LS (2018) Healthcare-based on cloud electrocardiogram system: a medical center experience in middle Taiwan. J Med Syst 42(3):39

    Article  Google Scholar 

  • Zhao J, Bose BK (2002) Evaluation of membership functions for fuzzy logic controlled induction motor drive. In: IECON 02 IEEE 2002 28th annual conference of the industrial electronics society, IEEE, vol 1, pp 229–234

  • Zhiqiang G, Lingsong H, Hang T, Cong L (2015) A cloud computing based mobile healthcare service system. In: 2015 IEEE 3rd international conference on smart instrumentation, measurement and applications (ICSIMA), IEEE, pp 1–6

  • Zimmermann HJ (2011) Fuzzy set theory—and its applications. Springer, New York

    Google Scholar 

Download references

Funding

This work was supported by the Pan African University, Institute of Basic Sciences, Technology and Innovation under the Commission of the African Union.

Author information

Authors and Affiliations

  1. Institute of Basic Sciences, Technology and Innovation, Pan African University, P. O Box 62000-00200, Nairobi, Kenya

    Geoffrey O. Barini

  2. Department of Pure and Applied Mathematics, Jomo Kenyatta University of Agriculture and Technology, P. O Box 62000-00200, Nairobi, Kenya

    Geoffrey O. Barini

  3. Department of Electrical and Telecommunication Engineering, Multimedia University of Kenya, P.O Box 15653- 00503, Nairobi, Kenya

    Livingstone M. Ngoo

  4. Department of Computing, Jomo Kenyatta University of Agriculture and Technology, P. O Box 62000-00200, Nairobi, Kenya

    Ronald W. Mwangi

Authors
  1. Geoffrey O. Barini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Livingstone M. Ngoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ronald W. Mwangi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Geoffrey O. Barini.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barini, G.O., Ngoo, L.M. & Mwangi, R.W. Application of a fuzzy unit hypercube in cardiovascular risk classification. Soft Comput 23, 12521–12527 (2019). https://doi.org/10.1007/s00500-019-03802-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-03802-0

Keywords

Search

Navigation