One-year renal graft survival prediction using a weighted decision tree classifier

  • Abstract
  • Keywords
  • References
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  • Abstract

    This study introduces a weighted decision tree algorithm for prediction of graft survival in renal transplantation using preoperative patient's data. The objective was to identify the preoperative attributes that affect the graft survival. Between the years 2000-2009, renal allotransplantation was carried out for 889 patients at Urology and Nephrology Center which is the subject matter of this study. The ID3 algorithm was chosen to build up the decision tree using the weka machine learning software. A modification was made on ID3 to refine the results. A weighted vector was introduced. The element of such a vector represents the weight of each attribute which was obtained by trial and error. The results indicated that the weighted algorithm was successful in predicting the graft survival after one year and identifying the attributes affecting graft survival.

    Keywords: Decision Tree, Data Mining, ID3 Algorithm, Graft Survival, Kidney Transplantation.

  • References

    1. A. Ravikumar, R. Saritha, and S. S. V. Chandra, "Recent Trends in computational prediction of renal transplantation outcomes," Inter. J. Comput. Appl., vol. 63, no. 12, pp. 33-37, Feb. 2013.
    2. D. T. Akomolafe, and A. Olutayo, "Using Data Mining Technique to Predict Cause of Accident and Accident Prone Locations on Highways," Amer. J. database Theory and appl., vol. 1, no. 3, pp. 26-38, Jan. 2012.
    3. D. Hand, H. Mannila, and P. Smyth, "Principles of data Mining," The MIT Press, 2001. R. Hu, "Medical data mining based on decision tree algorithm," Comput. inf. science, vol. 4, no. 5, Sep. 2011.
    4. D. Md. Farid, and Ch. M. Rahman, "Assigning Weights to training instances increases classification accuracy," Int. J. Data Min. Know. Management Process, vol. 3, no. 1, pp. 13-25, Jan. 2013.
    5. M. Slocum, "Decision making using ID3 algorithm," Insight: River Academic J., vol. 8, no. 2, 2012.
    6. N. Mathur, S. Kumar, S. Kumar, and R. Jindal ,"The Base Strategy for ID3 Algorithm of Data Mining Using Havrda and Charvat Entropy Based on Decision Tree," Inter. J. Inform. Elect. Eng., vol. 2, no. 2, pp. 253-258, Mar. 2012.
    7. L. Ramanathan, S. Dhanda, and S. Kumar, "Predicting students' performance using modified ID3 algorithm," Inter. J. Eng. Tech., vol. 5, no. 3, pp. 2491-2497, June-July 2013.
    8. V. Maduskar, and Y. Kelkar, "A new modified decision tree algorithm based on ID3," Int. J. Comput. Arch. Mob., vol. 1, no. 9, July 2013.
    9. L. M. Taft, R. S. Evans, C. R. Shyu, M. J. Egger, N. Chawla, J. A. Mitchell, S. N. Thornton, B. Bray, and M. Varner, "Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery," J. Biomed. Informat., vol. 42, pp. 356364, Apr. 2009.
    10. N. H. Barakat, A. P. Bradley, and N. H. Barakat, "Intelligible support vector machines for diagnosis of diabetes mellitus," IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 4, pp. 11141120, July 2010.
    11. J. Li, G. Serpen, S. Selman, M. Franchetti, M. Riesen, and C. Schneider, "Bayes net classifiers for prediction of renal status and survival period," World Academy of Science, Eng. And Tech., no. 63, pp. 144-150, 2010.




Article ID: 2334
DOI: 10.14419/ijet.v3i3.2334

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