A Web-based Fuzzy Inference System Based Tool for Cardiovascular Disease Risk Assessment

Palabras clave

Cardiovascular risk assessment
Evidence Based Medicine
Fuzzy Logic

Cómo citar

Yunda, L., Pacheco, D., & Millan, J. (2015). A Web-based Fuzzy Inference System Based Tool for Cardiovascular Disease Risk Assessment. Nova, 13(24), 7-16. https://doi.org/10.22490/24629448.1712


Developing a Web-based Fuzzy Inference Tool for cardiovascular risk assessment. The tool uses evidence-based medicine inference rules for membership classification. Methods. The system framework allows adding variables such as gender, age, weight, height, medication intake, and blood pressure, with various types of membership functions based on classification rules. Results. By inputting patient clinical data, the tool allows health professionals to obtain a prediction of cardiovascular risk. The tool can also be later used to predict other types of risks including cognitive and physical disease conditions.



Enfermedad Cardiovascular: principal causa de muerte en Colombia. Boletin Observatorio Nacional de Salud, No. 1, Diciembre 9, 2013. Instituto Nacional de Salud, Colombia.

D’Agostino, R.B., et al. (1998). Prediction of coronary heart disease using risk factor categories. Circulation, 97, 1837-1847

D’Agostino, R.B., et al. (2008). General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation, 117, 743–753

BF Gage et al. (2001). Validation of clinical classification schemes for predicting stroke. Results from the national registry of atrial fibrillation. JAMA, 285: 2864-2870.

Fuzzy Diagnosis, Ludmila I. Kuncheva and Friedrich Steimann, Artificial Intelligence in Medicine, No. 16 (1999) 121-128

Asbury AJ. Feedback control in anesthesia. Int J Clin Monit Comput 1997;14(1):1–10.

AsburyAJ,TzabarY.Fuzzy-logic new ways of thinking for anesthesia. Br J Anaesth 1995; 75(1):1–2.

Linkens DA, Shieh JS, Peacock JE. Hierarchical fuzzy modeling for monitoring depth of anesthesia. Fuzzy Sets Syst 1996;79(1):43–57.

Downs J, Harrison RF, Kennedy RL, Cross SS. Application of the fuzzy ARTMAP neural network model to medical pattern classification tasks. Artif Intell Med 1996;8(4):403–28.

Goldman JM, Cordova MJ. Advanced clinical monitoringconsiderations for real-time hemodynamic diagnostics. J Am Med Inf Assoc 1994;SS:752–5.

Grauel A, Ludwig LA, Klene G. ECG diagnostics by fuzzy decision making. Int J Uncertain Fuzziness Knowl Based Syst 1998;6(2):210–0.

Ham FM, Han S. Classification of cardiac arrhythmias using fuzzy artmap. IEEE Trans Biomed Eng 1996; 43(4):425–30.

Held CM, Roy RJ. Multiple-drug hemodynamic control by means of a supervisory fuzzy rule-based adaptive-control system-validation on a model. IEEE Trans Biomed Eng 1995; 42(4):371–85.

Axial Suspension Fuzzy PID Control for Axial Artificial Heart Pump. Changjun Zhao et Al. Applied Mechanics & Materials , 2014, Vol. 703, p323-326, 4p.

Automated fuzzy evaluation of CT scan heart slices for creating 3D/4D heart model. M. Bielecka et Al. Applied Soft Computing, May 2015, vol.30, pp. 179-89.

Expert System for Heart Problems. M. Eswara Rao, Dr. S. Govinda Rao, October 2014 | Vol 4, Issue 10,266-271.

Medical diagnosis of cardiovascular diseases using an intervalvalued fuzzy rule-based classification system. Jose Antonio Sanz et Al. 28 November 2013.

Fuzzy Inferencing for Coronary Artery Disease Screening. Anu Ragavi et Al. Dec 2014 | Vol 4, Issue 12,388-391.

A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. Vahid Khatibi et Al. Expert Systems with Applications, Volume 37, Issue 12, December

, Pages 8536–8542

Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. P.K. Anooj. Journal of King Saud University. Volume 24, Issue 1, January

, Pages 27-40.

A Fuzzy Expert System for Heart Disease Diagnosis. Ali Adeli et Al. Proceedings of the International MultiConference of Engineers and Computer Scientists 2010 Vool I, IMECS 2010, March 17 – 19, 2010, Hong Kong.

Fuzzy Sets Application To Healthcare Systems. Luisa L. Lazzari et Al. Fuzzy Economic Review Vol. XVII, No. 2, November 2012, p. 43-58.

Zadeh LA. Fuzzy sets. Inf Control 1965; 8:338–53.

Etude comparative de la classification ascendante hiérarchique et de la classification floue pour identifier cinq familles de voitures. G. Paviot, , Cahier de recherche, Laboratoire Orléanais de Gestion, 1997.

Fuzzy logic based system for classification of atrial fibrillation cardiac arrhythmias. Messaoud, A., Ecole Nat. et. Al. Electronics, Circuits and Systems, 2006. ICECS ’06 13th IEEE International Conference on.

Creative Commons License
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0.

Derechos de autor 2017 NOVA Publicación en Ciencias Biomédicas

Detalle de visitas

PDF: 130
Resumen: 156