Application of Meta learning in the detection of Lung Cancer

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-26 Number-1
Year of Publication : 2015
Authors : Sanjay Kumar Sen, Dr. B.K. Ratha
  10.14445/22312803/IJCTT-V26P106

MLA

Sanjay Kumar Sen, Dr. B.K. Ratha "Application of Meta learning in the detection of Lung Cancer". International Journal of Computer Trends and Technology (IJCTT) V26(1):32-36, August 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Now a days Cancer is the most vital cause of death for both men and women in the world wide. There are several types of cancer like lung cancer, breast cancer and prostrate cancer etc. Among those lung cancer is the most fatal disease. Worldwide, lung cancer continues to be the leading cause of cancer-related mortality in men and women alike 2. If these diseases are detected in early stage then is patient can be survived, but most of the time the diseases detected at later stage for which the mortality rate rises. This paper proposes a methodology using a data mining which could predict the lung cancer at an early stage thereby increasing the survival rate of the patient by five years. This paper proposes a methodology using a data mining which could predict the lung cancer at an early stage thereby increasing the survival rate of the patient. This paper proposes a methodology using a data mining which could predict the lung cancer at an early stage thereby increasing the survival rate of the patient by five years. The experimental result shows the performance analysis of different meta-learning algorithms and also compared on the basis of misclassification and correct classification rate, the error rate focuses True Positive, True Negative, False Positive and False Negative and Accuracy. This project aims for mining the relationship in lung cancer data for efficient classification. The data mining methods and techniques will be explored to identify the suitable methods and techniques for efficient classification of cancer dataset and in mining useful patterns.

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Keywords
Data mining, meta learning algorithm.