Heart Disease Prediction System Using Decision Tree and Naive Bayes Algorithm

Author(s): Subburaj Maheswari*, Ramu Pitchai.

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 15 , Issue 8 , 2019

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Abstract:

The huge information of healthcare data is collected from the healthcare industry which is not “mined” unfortunately to make effective decision making for the identification of hidden information. The end user support system is used as the prediction application for the heart disease and this paper proposes windows through the intelligent prediction system the instance guidance for the heart disease is given to the user. Various symptoms of the heart diseases are fed into the application. The user precedes the processes by checking the specific detail and symptoms of the heart disease. The decision tree (ID3) and navie Bayes techniques in data mining are used to retrieve the details associated with each patient. Based on the accurate result prediction, the performance of the system is analyzed.

Keywords: Intelligent prediction system, decision tree algorithm, knowledge representation, data mining, naive bayes algorithm, heart disease prediction.

[1]
Borton J, Shoham J. Mapping vulnerability to food insecurity: Tentative guidelines for WFP country offices. In: London: Relief and Development Institute 2011.
[2]
Breiman L, Friedman J, Olshen RA, Stone CJ. Classification and regression trees. In: Monterey, California, USA: Wadsworth, Inc. 2015.
[3]
Chapman P, Clinton J, Kerber R, et al. Step by step data mining guide, SPSS 2000 Available from: https://www.the-modeling-agency.com/crisp-dm.pdf
[4]
Charly K. Data mining for the enterprise. 31st Annual Hawaii International Conference on System Sciences. IEEE Comp. 2014; 7: 295-304.
[5]
Currey B. Mapping of areas liable to famine in Bangladesh 2012.
[6]
Downing TE. Regions/vulnerable groups in FEWS methodology Memorandum 2000.
[7]
Fayyad U. Data mining and knowledge discovery in databases: implications for scientific databases. In: Proceedings of the Ninth International Conference on Scientific and Statistical Database Management (Cat No97TB100150);. 1997 11-13 Aug; Olympia, WA, USA: IEEE. 2002;
[8]
Frankenberger T. Indicators and data collection methods for assessing household food security.In: Maxwell S. & Frankenberger T.. EDs Household Food Security: Concepts, Indicators, And Measurements: A Technical Review. New York, NY, USA and Rome: UNICEF and IFAD 1992.
[9]
Giudici P. Applied data mining: Statistical methods for business and industry. New York: John Wiley 2003.
[10]
Han J, Kamber M. Data mining concepts and techniques. In: Morgan Kaufmann Publishers 2006.
[11]
Ho TJ. Data mining and data warehousing. In: Prentice Hall 2005.
[12]
Kaur H, Wasan SK. Empirical study on applications of data mining techniques in healthcare. J Comput Sci 2014; 2(2): 194-200.
[13]
Mehmed K. Data mining: Concepts, models, methods and algorithms. In: New Jersey: John Wiley 2013.
[14]
Mohd H, Mohamed SHS. Acceptance model of electronic medical record. J Adv Inform Manag Stud 2010; 2(1): 75-92.
[15]
Pitchai R, Jayashri S, Raja J. Searchable encrypted data file sharing method using public cloud service for secure storage in cloud computing. J Wireless Pers Commun 2016; 90(2): 947-60.
[16]
Papadaniil CD, Hadjileontiadis LJ. Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features. IEEE J Biomed Health Inform 2014; 18(4): 1138-52.
[http://dx.doi.org/10.1109/JBHI.2013.2294399] [PMID: 25014929]
[17]
Liu Q, Yan BP, Yu C-M, Zhang Y-T, Poon CC, Poon CC. Attenuation of systolic blood pressure and pulse transit time hysteresis during exercise and recovery in cardiovascular patients. IEEE Trans Biomed Eng 2014; 61(2): 346-52.
[http://dx.doi.org/10.1109/TBME.2013.2286998] [PMID: 24158470]
[18]
Marzbanrad F, Kimura Y, Funamoto K, et al. Automated estimation of fetal cardiac timing events from Doppler ultrasound signal using hybrid models. IEEE J Biomed Health Inform 2014; 18(4): 1169-77.
[http://dx.doi.org/10.1109/JBHI.2013.2286155] [PMID: 24144677]
[19]
Pattini L, Sassi R, Cerutti S. Dissecting heart failure through the multiscale approach of systems medicine. IEEE Trans Biomed Eng 2014; 61(5): 1593-603.
[20]
Wang Y, Simaan MA. A suction detection system for rotary blood pumps based on the Lagrangian support vector machine algorithm. IEEE J Biomed Health Inform 2013; 17(3): 654-63.
[21]
Melillo P, De Luca N, Bracale M, Pecchia L. Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE J Biomed Health Inform 2013; 17(3): 727-33.
[http://dx.doi.org/10.1109/JBHI.2013.2244902] [PMID: 24592473]
[22]
Harle CA, Neill DB, Padman R. Information visualization for chronic disease risk assessment. IEEE Intell Syst 2012; 27(6): 81-5.
[http://dx.doi.org/10.1109/MIS.2012.112]
[23]
Blake CL, Mertz CJ. UCI machine learning databases 2004.Available from: . http://mlearn.ics.uci.edu/databases/heartdisease/


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Article Details

VOLUME: 15
ISSUE: 8
Year: 2019
Page: [712 - 717]
Pages: 6
DOI: 10.2174/1573405614666180322141259
Price: $58

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