Classification of Heart Disease Hungarian Data Using Entropy, Knnga Based Classifier and Optimizer
Keywords:Heart disease, neural network, support vector machine, genetic algorithm, k nearest neighbors.
To mine the useful information from massive medical databases data mining plays as imperative role. In data mining classification (supervised learning) which can be used to design model by describing significant data classed, where class attribute is involved in the construction of the classifier. In this work, we propose a methodology in which uses KNN classifier. It is simple, popular, more efficient and proficient algorithm for pattern recognition. The samples of the medical databases are classified on the basis of nearest neighbor in which medical database are massively found in nature and contains irrelevant and redundant attributes. The only KNN classifier produce less accurate results that is why we use hybrid approach of KNN and genetic algorithm (GA) to obtain more accurate results. To evaluate the performance of the proposed approach Hungarian dataset (UCI learning) is used to classify the attributes of heart disease. The genetic algorithm performs global research on complex large and multimodal landscapes which provide minimal solutions or search space. The experimental outcomes of accuracy parameter of proposed approach give more accurate and efficient results than the existing approach.
 Aziz, N. Ismail, and F. Ahmad, â€œMining Studentsâ€™ Academic Performanceâ€, Journal of Theoretical & Applied Information Technology, vol. 53, no. 3, 2013.
 S. Kiruthika Devi, S. Krishnapriya and Dristipona Kalita â€œPrediction of Heart Disease using Data Mining Techniquesâ€, Indian Journal of Science and Technology, Vol 9(39), DOI: 10.17485/ijst/2016/v9i39/102078, October 2016.
 G.Vaishali, V.Kalaivani â€œBig Data Analysis for Heart Disease Detection System Using Map Reduce Techniqueâ€, In proceeding of IEEE, 2016.
 Ankita Dewan, Meghna Sharma â€œPrediction of Heart Disease Using a Hybrid Technique in Data Mining Classificationâ€, In proceeding of IEEE 2015.
 B.Venkatalakshmi, M.V Shivsankar â€œHeart Disease Diagnosis Using Predictive Data miningâ€, International Conference on Innovations in Engineering and Technology (ICIETâ€™14) On 21st&22ndMarch, Volume 3, Special Issue 3. In proceeding of IJIRSET.
 S. U. Amin, K. Agarwal, and R. Beg, â€œGenetic Neural Network Based Data Mining in Prediction of Heart Disease Using Risk Factors,â€ in Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013), 2013, no. Ict, pp. 1227â€“1231.
 A. K. Sen, S. B. Patel, and D. P. Shukla, â€œA Data Mining Technique for Prediction of Coronary Heart Disease Using Neuro-Fuzzy Integrated Approach Two Level,â€ International Journal of Engineering and Computer Science, vol. 2, no. 9, pp. 1663â€“1671, 2013.
 B.Venkatalakshmi, M.V Shivsankar â€œHeart Disease Diagnosis Using Predictive Data miningâ€, International Journal of Innovative Research in Science, Engineering and Technology Volume 3, Special Issue 3, March 2014.
 Helma C, Gottmann E, Kramer S (2000) Knowledge discovery and data mining in toxicology. Statistical Methods in Medical Research 9: 329-358.
 Quinlan JR (1986) Decision trees and multi-valued attributes. In: Hayes, Michie D (eds.) Machine intelligence. Oxford University Press.
 Han J, Kamber M (2006) Data Mining Concepts and Techniques: Morgan Kaufmann Publishers.
 Bramer M (2007) Principles of data mining: Springer.
 K.Sudhakar , Dr. M. Manimekalai â€œStudy of Heart Disease Prediction using Data Miningâ€ International Journal of Advanced Research in Computer Science and Software Engineering 4(1), January - 2014, pp. 1157-1160.
 Shishir K. Shandilya, S. Jain, "Automatic opinion extraction from web documents", Proceeding of International Conference on Computer and Automation Engineering, pp. 351-355, 2009.
 Ashutosh Dubey and Shishir K. Shandilya,"Exploiting Need Of Data Mining Services in Mobile Computing Environments", Computational Intelligence and Communication Networks (CICN), 2010
 R. Chaure, Shishir K. Shandilya, â€œFirewall anomalies detection and removal techniques â€“ A surveyâ€, International Journal of Emerging Technologies, Vol. 1(1), pp. 71â€“74, 2010
 A.K. Dubey, Shishir K. Shandilya, "A comprehensive survey of grid computing mechanism in J2ME for effective mobile computing techniques," Industrial and Information Systems (ICIIS), pp.207-212, 2010
 Shishir K. Shandilya, S. Jain, "Opinion Extraction & Classification of Reviews from Web Documents", Advance Computing Conference IEEE International, 2009.
 Asha Khilrani, Shishir K. Shandilya, â€œImplementation of Userâ€™s Browse Log Monitoring Tool for Effective Web Usage Miningâ€, International Journal of Computer Science and Information Technologies, Vol. 2 (3) pp. 1061-1064, 2011.
 Shishir K. Shandilya, S Jain, â€œAutomatic Extraction and Classification of Opinions of Product Reviews from Web Documentsâ€, IUP Journal of Systems Management, 2011
 N Mishra, R Kumar, SK Shandilya, Credit Card Transaction Fraud Detection by using Hidden Markov Model, International Journal of Scientific Engineering and Technology, Volume No.1, Issue No.2 pp:139-142, 2277-1581, 2012
 Smita Shandilya, SK Shandilya, Tripta Thakur, Atulya K Nagar, Handbook of Research on Emerging Technologies for Electrical Power Planning, Analysis, and Optimization, 2016
 Shishir K. Shandilya, Smita Shandilya, Kusum Deep, Atulya K. Nagar, Handbook of Research on Soft Computing and Nature-Inspired Algorithms, 2017
 Shishir K. Shandilya, Suneet K. Gupta, A Comprehensive Survey on Authorâ€™s Trait on Blog Data, International Journal of Advanced Engineering & Application, 2011
 S Shandilya, T Thakur, SK Shandilya, Transmission Network Expansion Planning Considering N-1 Contingency, Proceedings of International Conference on Control, Communication and Power Engineering, Elsevier, 2013
 Dr saed sayad,â€University of toronto http://chem-eng.utoronto.ca/~data mining.
 Nitin Bhatia, vandanaâ€Survey on nearest neighbor techniquesâ€IJCSIS,Vol 80,no 2(2010).
View Full Article:
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aÂ Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (SeeÂ The Effect of Open Access).