Classification of Heart Disease Hungarian Data Using Entropy, Knnga Based Classifier and Optimizer

  • Authors

    • Shweta Gupta
    • . .
  • 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. 



  • References

    1. [1] Aziz, N. Ismail, and F. Ahmad, “Mining Students’ Academic Performanceâ€, Journal of Theoretical & Applied Information Technology, vol. 53, no. 3, 2013.

      [2] S. Kiruthika Devi, S. Krishnapriya and Dristipona Kalita “Prediction 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.

      [3] G.Vaishali, V.Kalaivani “Big Data Analysis for Heart Disease Detection System Using Map Reduce Techniqueâ€, In proceeding of IEEE, 2016.

      [4] Ankita Dewan, Meghna Sharma “Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classificationâ€, In proceeding of IEEE 2015.

      [5] B.Venkatalakshmi, M.V Shivsankar “Heart Disease Diagnosis Using Predictive Data miningâ€, International Conference on Innovations in Engineering and Technology (ICIET’14) On 21st&22ndMarch, Volume 3, Special Issue 3. In proceeding of IJIRSET.

      [6] S. U. Amin, K. Agarwal, and R. Beg, “Genetic Neural Network Based Data Mining in Prediction of Heart Disease Using Risk Factors,†in Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013), 2013, no. Ict, pp. 1227–1231.

      [7] A. K. Sen, S. B. Patel, and D. P. Shukla, “A Data Mining Technique for Prediction of Coronary Heart Disease Using Neuro-Fuzzy Integrated Approach Two Level,†International Journal of Engineering and Computer Science, vol. 2, no. 9, pp. 1663–1671, 2013.

      [8] B.Venkatalakshmi, M.V Shivsankar “Heart Disease Diagnosis Using Predictive Data miningâ€, International Journal of Innovative Research in Science, Engineering and Technology Volume 3, Special Issue 3, March 2014.

      [9] Helma C, Gottmann E, Kramer S (2000) Knowledge discovery and data mining in toxicology. Statistical Methods in Medical Research 9: 329-358.

      [10] Quinlan JR (1986) Decision trees and multi-valued attributes. In: Hayes, Michie D (eds.) Machine intelligence. Oxford University Press.

      [11] Han J, Kamber M (2006) Data Mining Concepts and Techniques: Morgan Kaufmann Publishers.

      [12] Bramer M (2007) Principles of data mining: Springer.

      [13] K.Sudhakar , Dr. M. Manimekalai “Study of Heart Disease Prediction using Data Mining†International Journal of Advanced Research in Computer Science and Software Engineering 4(1), January - 2014, pp. 1157-1160.

      [14] Shishir K. Shandilya, S. Jain, "Automatic opinion extraction from web documents", Proceeding of International Conference on Computer and Automation Engineering, pp. 351-355, 2009.

      [15] Ashutosh Dubey and Shishir K. Shandilya,"Exploiting Need Of Data Mining Services in Mobile Computing Environments", Computational Intelligence and Communication Networks (CICN), 2010

      [16] R. Chaure, Shishir K. Shandilya, “Firewall anomalies detection and removal techniques – A surveyâ€, International Journal of Emerging Technologies, Vol. 1(1), pp. 71–74, 2010

      [17] 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

      [18] Shishir K. Shandilya, S. Jain, "Opinion Extraction & Classification of Reviews from Web Documents", Advance Computing Conference IEEE International, 2009.

      [19] Asha Khilrani, Shishir K. Shandilya, “Implementation of User’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.

      [20] Shishir K. Shandilya, S Jain, “Automatic Extraction and Classification of Opinions of Product Reviews from Web Documentsâ€, IUP Journal of Systems Management, 2011

      [21] 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

      [22] Smita Shandilya, SK Shandilya, Tripta Thakur, Atulya K Nagar, Handbook of Research on Emerging Technologies for Electrical Power Planning, Analysis, and Optimization, 2016

      [23] Shishir K. Shandilya, Smita Shandilya, Kusum Deep, Atulya K. Nagar, Handbook of Research on Soft Computing and Nature-Inspired Algorithms, 2017

      [24] Shishir K. Shandilya, Suneet K. Gupta, A Comprehensive Survey on Author’s Trait on Blog Data, International Journal of Advanced Engineering & Application, 2011

      [25] 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

      [26] Dr saed sayad,â€University of toronto mining.

      [27] Nitin Bhatia, vandanaâ€Survey on nearest neighbor techniquesâ€IJCSIS,Vol 80,no 2(2010).

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    Gupta, S., & ., . (2018). Classification of Heart Disease Hungarian Data Using Entropy, Knnga Based Classifier and Optimizer. International Journal of Engineering & Technology, 7(4.5), 292-296.