Abstract
Heart disease is one of the significant reason of death and disability. The shortage of Doctors, experts and ignoring patient symptoms lead to big challenge that may cause death, disability to the patient. Therefore, we need expert system that serve as an analysis tool to discover hidden information and patterns in hear disease medical data. Data mining is a cognitive procedure of discovering the hidden approach patterns from large data set. The available massive data can used to extract useful information and relate all attributes to make a decision. Various techniques listed and tested here to understand the accuracy level of each. In previous studies, researchers expressed their effort on finding best prediction model. This paper proposes new heart disease prediction system that combine all techniques into one single algorithm, it called hybridization. The result confirm that accurate diagnose can be taken by using a combined model from all techniques.
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References
Kumari, M., Godara, S.: Comparative study of data mining classification methods in cardiovascular disease prediction 1 (2011)
Purusothaman, G., Krishnakumari, P.: A survey of data mining techniques on risk prediction: heart disease. Indian J. Sci. Technol. 8(12) (2015)
Parthiban, L., Subramanian, R.: Intelligent heart disease prediction system using CANFIS and genetic algorithm. Int. J. Biol. Biomed. Med. Sci. 3(3) (2008)
Kalaiselvi, C., Nasira, G.: Prediction of heart diseases and cancer in diabetic patients using data mining techniques. Indian J. Sci. Technol. 8(14) (2015)
Santhanam, T., Ephzibah, E.: Heart disease prediction using hybrid genetic fuzzy model. Indian J. Sci. Technol. 8(9), 797–803 (2015)
Yeh, Y.-C., et al.: A reliable feature selection algorithm for determining heartbeat case using weighted principal component analysis. In: 2016 International Conference on System Science and Engineering (ICSSE). IEEE (2016)
Dubey, V.K., Saxena, A.K.: Hybrid classification model of correlation-based feature selection and support vector machine. In: IEEE International Conference on Current Trends in Advanced Computing (ICCTAC). IEEE (2016)
Krishnaiah, V., Narsimha, G., Chandra, N.S.: Heart disease prediction system using data mining technique by fuzzy K-NN approach. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds.) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1, pp. 371–384. Springer, Cham (2015)
Dominic, V., Gupta, D., Khare, S.: An effective performance analysis of machine learning techniques for cardiovascular disease. Appl. Med. Inform. 36(1), 23–32 (2015)
Alizadehsani, R., et al.: A data mining approach for diagnosis of coronary artery disease. Comput. Methods Programs Biomed. 111(1), 52–61 (2013)
Giri, D., et al.: Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowl.-Based Syst. 37, 274–282 (2013)
Al-Milli, N.: Backpropagation neural network for prediction of heart disease. J. Theor. Appl. Inf. Technol. 56(1), 131–135 (2013)
Dbritto, R., Srinivasaraghavan, A., Joseph, V.: Comparative analysis of accuracy on heart disease prediction using classification methods. Int. J. Appl. Inf. Syst. (IJAIS) (2016). ISSN 2249-0868
Pandey, A.K., et al.: Datamining clustering techniques in the prediction of heart disease using attribute selection method. Heart Dis. 14, 16–17 (2013)
Shouman, M., Turner, T., Stocker, R.: Using decision tree for diagnosing heart disease patients. In: Proceedings of the Ninth Australasian Data Mining Conference-Volume 121. Australian Computer Society, Inc. (2011)
Singh, N., Firozpur, P., Jindal, S.: Heart disease prediction system using hybrid technique of data mining algorithms (2018)
Agrawal, A., et al.: Disease prediction using machine learning (2018)
Shirwalkar, N., et al.: Human heart disease prediction system using data mining techniques (2018)
Cleveland Database. https://archive.ics.uci.edu/ml/datasets/heart+Disease
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Tarawneh, M., Embarak, O. (2019). Hybrid Approach for Heart Disease Prediction Using Data Mining Techniques. In: Barolli, L., Xhafa, F., Khan, Z., Odhabi, H. (eds) Advances in Internet, Data and Web Technologies. EIDWT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-12839-5_41
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DOI: https://doi.org/10.1007/978-3-030-12839-5_41
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