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.
Graphical Abstract
[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.
[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.
[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.
Download PDF Flyer