Fuzzy based Risk Predictive Model for Cardiovascular Complication of Patient with Type 2 Diabetes Mellitus and Hypertension

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Napa Rachata Punnarumol Temdee Worasak Rueangsirarak Chayapol Kamyod


Cardiovascular diseases are chronic diseases that cause serious morbidity and mortality worldwide. Unfortunately, the patients with type 2 diabetes mellitus and hypertension have a high risk of having a cardiovascular complication. For these reasons, patients with type 2 diabetes mellitus and hypertension should be aware of cardiovascular complication along their healthcare journey. To prevent cardiovascular complication from diabetes and hypertension, accurate risk prediction is required for a long term self-management process. Consequently, this paper proposes a fuzzy logic based method for predicting cardiovascular risk particularly for a patient with type 2 diabetes mellitus and hypertension. This paper also proposes a set of factors based on the patient’s lifestyle as the key factors besides clinical factors because of their implicit impact on the quality of life of the patient. The proposed model thus employs 15 predictors for both clinical and lifestyle risk factors. Additionally, the proposed model is constructed based on the scientific data and implicit knowledge of the experts. The experiment with 121 patients shows that the proposed prediction model provides 96.69% accuracy compared to those decided by the experts.


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How to Cite
N. Rachata, P. Temdee, W. Rueangsirarak, and C. Kamyod, “Fuzzy based Risk Predictive Model for Cardiovascular Complication of Patient with Type 2 Diabetes Mellitus and Hypertension”, ECTI Transactions on Computer and Information Technology (ECTI-CIT), vol. 13, no. 1, pp. 41-50, Jun. 2019.


[1] World Health Organization, “Noncommunicable diseases,” [Online]. Available: https://www.who.int/en/news-room/fact-sheets/detail/noncommunicable-diseases. [09-April-2019].
[2] Centers for Disease Control and Prevention, “Chronic Disease Prevention and Health Promotion,” [Online]. Available: https://www.cdc.gov/chronicdisease/index.htm. [09-April-2019].
[3] World Health Organization, “Cardiovascular diseases,” [Online]. Available: https://www.who.int/mediacentre/factsheets/fs317/en/. [09-April-2019].
[4] S. Mendis, P. Puska, B. Norrving and World Health Organization, Global atlas on cardiovascular disease prevention and control, World Health Organization, Geneva, 2011.
[5] World Health Organization, “Diabetes,” [Online]. Available: https://www.who.int/mediacentre/factsheets/fs312/en/. [09-April-2019].
[6] C. D. Mathers and D. Loncar, “Projections of global mortality and burden of disease from 2002 to 2030,” PLoS medicine, Vol. 3, No. 11, p. e442, 2006.
[7] J. R. Sowers, M. Epstein and E. D. Frohlich, “Diabetes, hypertension, and cardiovascular disease an update,” Hypertension, Vol. 37, No. 4, pp. 1053-1059, 2001.
[8] N. R. Campbell, R. E. Gilbert, L. A. Leiter, P. Larochelle, S. Tobe, A. Chockalingam, R. Ward, D. Morris, R. T. Tsuyuki and S. B. Harris, “Hypertension in people with type 2 diabetes Update on pharmacologic management,” Canadian Family Physician, Vol. 57, No. 9, pp. 997-1002, 2011.
[9] D. I. Pavlou, S. Α. Paschou, P. Anagnostis, M. Spartalis, E. Spartalis, A. Vryonidou, N. Tentolouris and G. Siasos, “Hypertension in patients with type 2 diabetes mellitus: Targets and management,” Maturitas, Vol. 112, pp. 71-77, 2018.
[10] I. Martín-Timón, C. Sevillano-Collantes, A. Segura-Galindo and F. J. del Cañizo-Gómez, “Type 2 diabetes and cardiovascular disease: have all risk factors the same strength?,” World journal of diabetes, Vol. 5, No. 4, pp. 444-470, 2014.
[11] P. W. Wilson, R. B. D’Agostino, D. Levy, A. M. Belanger, H. Silbershatz and W. B. Kannel, “Prediction of coronary heart disease using risk factor categories,” Circulation, Vol. 97, No. 18, pp. 1837-1847, 1998.
[12] R. B. D’Agostino Sr, S. Grundy, L. M. Sullivan, P. Wilson and CHD Risk Prediction Group, “Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation,” Jama, Vol. 286, No. 2, pp. 180-187, 2001.
[13] R. M. Conroy, K. Pyörälä, A. P. Fitzgerald, S. Sans, A. Menotti, G. De Backer, D. De Bacquer, P. Ducimetière, P. Jousilahti, U. Keil, I. Njølstad, R. G. Oganov, T. Thomsen, H. Tunstall-Pedoe, A. Tverdal, H. Wedel, P. Whincup, L. Wilhelmsen and I. M. Graham, “Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project,” European heart journal, Vol. 24, No. 11, pp. 987-1003, 2003.
[14] P. Sritara, S. Cheepudomwit, N. Chapman, M. Woodward, C. Kositchaiwat, S. Tunlayadechanont, T. Sura, B. Hengprasith, V. Tanphaichitr, S. Lochaya, B. Neal, S. Tanomsup and T. Yipintsoi, “Twelve-year changes in vascular risk factors and their associations with mortality in a cohort of 3499 Thais: the Electricity Generating Authority of Thailand Study,” International journal of epidemiology, Vol. 32, No. 3, pp. 461-468, 2003.
[15] World Health Organization, Prevention of cardiovascular disease: Guidelines for assessment and management of cardiovascular risk, WHO Document Production Services, Geneva, 2007.
[16] R. B. D’Agostino, R. S. Vasan, M. J. Pencina, P. A. Wolf, M. Cobain, J. M. Massaro and W. B. Kannel, “General cardiovascular risk profile for use in primary care the Framingham Heart Study,” Circulation, Vol. 117, No. 6, pp. 743-753, 2008.
[17] J. Hippisley-Cox, C. Coupland, J. Robson, A. Sheikh and P. Brindle, “Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore,” BMJ, pp. 1-15, 2009.
[18] M. J. Pencina, R. B. D'Agostino, M. G. Larson, J. M. Massaro and R. S. Vasan, “Predicting the 30-year risk of cardiovascular disease The Framingham Heart Study,” Circulation, Vol. 119, No. 24, pp. 3078-3084, 2009.
[19] C. Eswaran, R. Logeswaran and A. R. A. Rahman, “Prediction models for early risk detection of cardiovascular event,” Journal of medical systems, Vol. 36, No. 2, pp. 521-531, 2012.
[20] P. Nordet, S. Mendis, A. Dueñas, R. de la Noval, N. Armas, I. L de la Noval and H. Pupo, “Total cardiovascular risk assessment and management using two prediction tools, with and without blood cholesterol,” MEDICC review, Vol. 15, No. 4, pp. 36-40, 2013.
[21] D. Otgontuya, S. Oum, B. S. Buckley and R. Bonita, “Assessment of total cardiovascular risk using WHO/ISH risk prediction charts in three low and middle income countries in Asia,” BMC public health, Vol. 13, No. 1, 2013.
[22] P. M. Ridker and P. M. Cook, “Statins: new American guidelines for prevention of cardiovascular disease”, The Lancet, Vol. 382, No. 9907, pp. 1762-1765, 2013.
[23] T. P. L. Nguyen, C. C. M. Schuiling-Veninga, T. B. Y. Nguyen, V. T. T. Hang, E. P. Wright and M. J. Postma, “Models to predict the burden of cardiovascular disease risk in a rural mountainous region of Vietnam,” Value in Health Regional Issues, Vol. 3, pp. 87-93, 2014.
[24] S. Selvarajah, G. Kaur, J. Haniff, K. C. Cheong, T. G. Hiong, Y. van der Graaf and M. L. Bots, “Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population,” International journal of cardiology, Vol. 176, No. 1, pp. 211-218, 2014.
[25] World Health Organization, WHO/ISH risk prediction charts for 14 WHO epidemiological sub-regions, World Health Organization, Geneva, 2007.
[26] J. Cederholm, K. Eeg-Olofsson, B. Eliasson, B. Zethelius, P. M. Nilsson and S. Gudbjörnsdottir, “Risk prediction of cardiovascular disease in type 2 diabetes A risk equation from the Swedish National Diabetes Register,” Diabetes care, Vol. 31, No. 10, pp. 2038-2043, 2008.
[27] Emerging Risk Factors Collaboration, “Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies,” The Lancet, Vol. 375, No. 9733, pp. 2215-2222, 2010.
[28] T. Robinson, C. R. Elley, S. Wells, E. Robinson, T. Kenealy, R. Pylypchuk, D. Bramley, B. Arroll, S. Crengle, T. Riddell, S. Ameratunga, P. Metcalf and P. Drury, “New Zealand Diabetes Cohort Study cardiovascular risk score for people with Type 2 diabetes: validation in the PREDICT cohort,” Journal of primary health care, Vol. 4, No. 3, pp. 181-188, 2012.
[29] S. Van Dieren, J. W. J. Beulens, A. P. Kengne, L. M. Peelen, G. E. H. M. Rutten, M. Woodward, Y. T. van der Schouw and K. G. M. Moons, “Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review,” Heart, Vol. 98, No. 5, pp. 360-369, 2012.
[30] A. Willis, M. Davies, T. Yates and K. Khunti, “Primary prevention of cardiovascular disease using validated risk scores: a systematic review,” Journal of the Royal Society of Medicine, Vol. 105, No. 8, pp. 348-356, 2012.
[31] P. Radha and B. Srinvasan, “Hybrid Prediction Model for the Risk of Cardiovascular Disease in Type-2 Diabetic Patients,” International Journal of Advance Research in Computer Science and Management Studies, Vol. 2, No. 10, pp. 52-63, 2014.
[32] A. S. Kumar, Fuzzy Expert Systems for Disease Diagnosis, PA : Medical Information Science Reference, Hershey, 2015.
[33] A. Adeli and M. Neshat, “A fuzzy expert system for heart disease diagnosis,” Proceeding of International Multi Conference of Engineers and Computer Scientist (IMECS), pp.134-139, 2010.
[34] B. A. Ojokoh, M. O. Omisore, O. W. Samuel and T. O. Ogunniyi, “A fuzzy logic based personalized recommender system,” International Journal of Computer Science and Information Technology & Security (IJCSITS), Vol. 2, pp. 1008-1015, 2012.
[35] S. Kumar and H. Jain, “A Fuzzy Logic Based Model for Life Insurance Underwriting When Insurer is Diabetic,” International Journal of Fuzzy Systems and Rough Systems, Vol. 5, No.1, pp. 51-58, 2012.
[36] G. H. Kulkarni and P. G. Waingankar, “Fuzzy logic based traffic light controller,” Proceeding of International Conference on Industrial and Information Systems, pp. 107-110, 2007.
[37] G. Dudek, A. Strzelewicz, M. Krasowska, A. Rybak and R. Turczyn, “Fuzzy analysis of the cancer risk factor,” Acta Physica Polonica-Series B Elementary Particle Physics, Vol. 43, No. 5, pp. 947-958, 2012.
[38] A. L. Alonso, O. A. Rosas-Jaimes and J. A. Suárez-Cuenca, “Fuzzy Logic Assisted Diagnosis for Atherogenesis Risk,” Proceeding of 12th IFAC Symposium on Computer Applications in Biotechnology, pp. 255-259, 2013.
[39] K. K. Oad, X. DeZhi and P. K. Butt, “A fuzzy rule based approach to predict risk level of heart disease,” Global Journal of Computer Science and Technology, Vol. 14, No. 3-C, pp. 16-22, 2014.
[40] J. Kim, J. Lee and Y. Lee, “Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree,” Healthcare informatics research, Vol. 21, No. 3, pp. 167-174, 2015.
[41] K. Shang and Z. Hossen, “Applying fuzzy logic to risk assessment and decision-making,” Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries, pp. 1-59, 2013.