Knowledge Discovery from Heart Disease Dataset Using Optimized Neural Network

  • R. Chitra
  • V. Seenivasagam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

Risk Level Prediction at early stage will significantly reduce the risk of Heart Disease. In this paper a novel intelligent technique is proposed to discover the knowledge about the risk of Heart Disease using Optimized Neural Network. A Feed Forward Neural Network optimized using Genetic Algorithm is used for prediction. The network parameters hidden neurons, momentum factor and learning rate are optimized using Genetic Algorithm and the performance is analyzed for standard heart disease dataset and clinical dataset.The classification results prove that the proposed Genetic Optimized Neural Network highly contribute the physician to diagnosis the disease early by discover the knowledge of risk.

Keywords

Genetic Algorithm Neural Network Risk Level Prediction Optimization Heart Disease 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anooj, P.K.: Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University Computer and Information Sciences 24(1), 27–40 (2012)CrossRefGoogle Scholar
  2. 2.
    Global status report on no communicable diseases, World Health Organization (2010)Google Scholar
  3. 3.
    Soni, J., Ansari, U., Sharma, D.: Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction. International Journal of Computer Applications (0975 8887) 17(8) (2011)Google Scholar
  4. 4.
    Srinivas, K., Raghavendra Rao, G., Govardhan, A.: Survey on prediction of heart morbidity using data mining techniques. International Journal of Data Mining & Knowledge Management Process (IJDKP) 1(3), 14–34 (2011)CrossRefGoogle Scholar
  5. 5.
    Usha Rani, K.: Analysis of heart diseases dataset using neural network approach. International Journal of Data Mining & Knowledge Management Process (IJDKP) 1(5), 1–8 (2011)CrossRefGoogle Scholar
  6. 6.
    Palaniappan, S., Awang, R.: Intelligent Heart Disease Prediction System Using Data Mining Techniques, pp. 108–115. IEEE (2008)Google Scholar
  7. 7.
    Karegowda, A.G., Manjunath, A.S., Jayaram, M.A.: Application of Genetic Algorithm Optimized Neural Network Connection Weights for Medical Diagnosis of Pima Indians Diabetes. International Journal on Soft Computing 2(2), 15–23 (2011)CrossRefGoogle Scholar
  8. 8.
    Wilamowski, B.W., Chen, Y., Malinowski, A.: Efficient Algorithm for Training Neural Networks with one Hidden Layer. In: Proceedings on the International Conference on Neural Networks, San Diego, CA (1997)Google Scholar
  9. 9.
    Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Applications. IEEE Transactions on Industrial Electronics 43(5) (October 1996)Google Scholar
  10. 10.
    Tian, X., Song, T., Liu, Y.: Improving the structure and the parameter of the BP nerve network with Genetic Algorithm. Journal of Dalian University of Technology 2(6), 69–71 (2004)Google Scholar
  11. 11.
    Weihong, Z., Shunqing, X.: Optimization of BP Neural Network Classifier Using Genetic Algorithm. In: Du, Z. (ed.) Intelligence Computation and Evolutionary Computation. AISC, vol. 180, pp. 599–605. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • R. Chitra
    • 1
  • V. Seenivasagam
    • 2
  1. 1.Department of Computer Science and EngineeringNoorul Islam Centre for Higher EducationKanyakumari DistrictIndia
  2. 2.Department of Computer Science and EngineeringNational Engineering CollegeThoothukudi DistrictIndia

Personalised recommendations