Abstract
The forecast of a Myocardial infarction in youngsters is a significant challenge for cardiac experts and technologists because its symptoms and chemical levels of biomarkers in the blood are different from mature adults. Deployment of an intelligent method in this context is also a challenging task. The proposed method of this article for heart diseases is Mamdani fuzzy inference system. This intelligent system takes 14 different input parameters. These are CP (“chest pain”), BP (“blood pressure”), LDL (“bad cholesterol”), ED (“energy drink”), BS (“blood sugar”), HB (“heartbeat”), FH (“family history”), and LOP (“lack of physical activity”), HOA (“history of autoimmune disease”), HD (“unhealthy diet”), and D (“drug use”). The proposed system is able to predict the heart situation as an output which is named as “Chance”. The proposed system indicates whether Myocardial infarction risk is moderate, mild or severe on the basis of some mathematical calculations. For this purpose, various type of standard mathematical functions has been used. The proposed system is specifically designed for teenagers’ heart health issue and uses more variables as compared to any other intelligent system, so it gives more accurate results about teenagers’ heart health than any other system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kumar, A.S.: Diagnosis of heart disease using advanced fuzzy resolution mechanism. Int. J. Sci. Appl. Inf. Technol. (IJSAIT) 2(2), 22–30 (2013)
Phil, M.: Predicting heart attack using fuzzy C means clustering algorithm. Int. J. Latest Trends Eng. Technol. (IJLTET) 5(3), 439–443 (2015)
Chitra, R.: Heart attack prediction system using fuzzy C means classifier. IOSR J. Comput. Eng. 14(2), 23–31 (2013)
Huq, M., Chakraborty, C., Khan, R.M., Tabassum, T.: Heart attack detection using smart phone. Int. J. Technol. Enhancements Emerg. Eng. Res. 1(3), 23–27 (2013)
Kumar, S., Kaur, G.: Detection of heart diseases using fuzzy logic. Int. J. Eng. Trends Technol. 4(6), 2694–2699 (2013)
Barman, M., Pal Choudhury, J.: A fuzzy rule base system for the diagnosis of heart disease. Int. J. Comput. Appl. 57(7), 975–8887 (2012)
Sushil, S., Ram, S., Sikich, S., Ram, A.M.S.: Fuzzy expert systems (FES) for medical diagnosis. Int. J. Comput. Appl. 63(11), 975–8887 (2013)
Chakraborty, C., Khan, R.M., Tabassum, T.: A fuzzy-mining approach for solving rule based expert system unwieldiness in medical domain. Neural Netw. World 23(5), 435–450 (2015)
Sowmya, C., Sumitra, P.: Analytical study of heart disease diagnosis using classification techniques. In: 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), pp. 1–5 (2017)
Srinivas, K., Rao, G.R., Govardhan, A.: An analysis of coronary heart disease and perdition of heart attack in coal mining regions using data mining techniques. In: 2010 5th International Conference on Computer Science & Education, pp. 1344–1349 (2010)
Kubler, S., Derigent, W., Voisin, A., Robert, J., Le Traon, Y.: Knowledge-based consistency index for fuzzy pairwise comparison matrices. In: 2017 IEEE International Conference on Fuzzy Systems (Fuzz-IEEE), pp. 1–7 (2017)
Chen, C., Wang, C., Wang, Y.T., Wang, P.T.: Fuzzy logic controller design for intelligent robots. Math. Problems Eng. 2017, 12 (2017)
Song, L., Wang, H., Chen, P.: Step-by-step fuzzy diagnosis method for equipment based on symptom extraction and trivalent logic fuzzy diagnosis theory. IEEE Trans. Fuzzy Syst. 26, 3467–3478 (2018)
Whig, P.: Fuzzy logic implementation of photo catalytic sensor. Int. Robot. Autom. J. 2(3), 15–19 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hassan, A., Bilal, H.M., Khan, M.A., Khan, M.F., Hassan, R., Farooq, M.S. (2019). Enhanced Fuzzy Resolution Appliance for Identification of Heart Disease in Teenagers. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_3
Download citation
DOI: https://doi.org/10.1007/978-981-13-6052-7_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6051-0
Online ISBN: 978-981-13-6052-7
eBook Packages: Computer ScienceComputer Science (R0)