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Lung Cancer

Prediction of Lung Cancer Patient Survival via Supervised Machine Learning Classification Techniques. Chip M. Lyncha , Behnaz Abdollahib , Joshua D. Fuquac , Alexandra R. de Carloc , James A. Bartholomaic , Rayeanne N. Balgemannc , Victor H. van Berkeld , Hermann B. Frieboesc,e

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Lung Cancer

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  1. Prediction of Lung Cancer Patient Survival via Supervised Machine Learning Classification Techniques Chip M. Lyncha, BehnazAbdollahib, Joshua D. Fuquac, Alexandra R. de Carloc, James A. Bartholomaic, Rayeanne N. Balgemannc, Victor H. van Berkeld, Hermann B. Frieboesc,e University of Louisville, KY, USA International Journal of Medical Informatics, 2017 Presented By: Firas Gerges (fg92)

  2. Lung Cancer

  3. Lung Cancer Survivability

  4. Previous Work

  5. Data

  6. Data • 10442 patient record • Each record include:

  7. Data • The goal is to successfully and accurately predict the number of months that patient will be alive from data of diagnosis. • Survival time distribution within the data set:

  8. Machine Learning Techniques

  9. Linear Regression

  10. Decision Trees

  11. Gradient Boosting Machines (GBM)

  12. Support Vector Machine

  13. Custom Ensemble

  14. Results

  15. Performance measure

  16. Performance Measurement • The Root Mean Square Error (RMSE) is used to describe the performance of each technique

  17. Performance analysis

  18. Performance

  19. Performance analysis

  20. Decision tree

  21. Discussion

  22. Discussion

  23. Conclusion

  24. Conclusion • Future work includes further exploring the factors and limitation of used models, and revaluating the inputs for the selected models

  25. Thank You

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