Abstract
Heart disease (cardiovascular disease) is one of the core issues prevalent in this generation. Every year, millions of people die due to various heart diseases. The problem occurs due to hereditary or changes in life styles. Various data mining techniques are used in order to predict heart diseases. Data mining increases the accuracy, precision, and sensitivity of the model being used. In the proposed hybrid approach for predicting heart disease using optimization clustering and image processing (Hy-OCIP) model, a hybrid approach is used to predict heart diseases with the help of optimization, clustering, and image processing. After the heart image is being processed, centroid clustering is used for clustering the processed imaged into a set of chromosomes for optimization. The optimization method used for our model is genetic algorithm. The same methods are performed for both, a healthy and a heart patient. As a result, the model used in this research is able to distinguish between a normal patient and a heart patient by a hybrid combination of image processing, clustering, and optimization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Krishnaiah, G.N.V.: Heart disease prediction system using data mining techniques and intelligent fuzzy approach: a review. Int. J. Comput. Appl. 136(2), 43–51 (2016)
Iftikhar, K.F.: An evolution based hybrid approach for heart diseases classification and associated risk factors identification. Int. J. Med. Sci. 28(8) (2016)
Kausar, S.P.: Systematic analysis of applied data mining based optimization algorithms in clinical attribute extraction and classification for diagnosis of cardiac patients. Appl. Intell. Optim. Biol. Med. 96, 217–231 (2015)
Paul, A.: Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease. In: 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), May 2016
Majeed, U.: Data mining in healthcare for heart diseases. Int. J. Innov. Appl. Stud. 10(4), 1312–1322 (2015)
Dewan, A.: Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification. IEEE, Mar 2015
Uyar, K.: Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. Proc. Comput. Sci. 120, 588–593 (2017)
Shouman, T.S.M.: Using Data Mining Techniques in Heart Disease Diagnosis and Treatment. IEEE (2012) (Article)
Singh, R.: Prediction of heart disease by clustering and classification techniques. Int. J. Comput. Sci. Eng. 7(5) (2019)
Cincy, R.: A Survey on Predicting Heart Disease Using Data Mining Technique. IEEE, pp. 253–255 (2018)
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 Singapore Pte Ltd.
About this paper
Cite this paper
Paul, N.K., Harsha, K.G., Kumar, P., Philip, S., George, J.P. (2021). Hybrid Approach for Predicting Heart Disease Using Optimization Clustering and Image Processing. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems. ICTIS 2020. Smart Innovation, Systems and Technologies, vol 195. Springer, Singapore. https://doi.org/10.1007/978-981-15-7078-0_33
Download citation
DOI: https://doi.org/10.1007/978-981-15-7078-0_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7077-3
Online ISBN: 978-981-15-7078-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)