ISSN : 2319-7323

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE ENGINEERING

ABSTRACT

Title	:	Applying Hybrid Classification Data Mining Techniques to Improve Lung Cancer Diagnosis
Authors	:	Diljot Singh, Prabhdeep Singh, Dr. Rajbir Kaur
Keywords	:	Lung Cancer. Data Mining, Classification, Weka, SMOTE
Issue Date	:	Jan-Feb 2020
Abstract	:	Enormous deaths are being caused by Lung cancer, one in all the harmful disease around the world. The only possible approach to improve a patient's probability for survival is the early detection of it and if it is detected beforehand, it can help to fix the disease completely. So the demand of the techniques to find the existence of cancer nodule within the early stage is expanding. Prior diagnosis of lung cancer will definitevely saves huge lives, and failing to do so will lead to severe problems resulting in unexpected fatal finish. Data mining is an incredible method to help individuals in their wellbeing, Scientific and Engineering. It uses a learning strategy to understand the data patterns. Those techniques are extracting the key information from the huge databases, which helps us to find the pattern and the relationship from the data. The Hybrid classifier is very useful in the classification of lung cancer dataset as it gives a lot higher exactness than alternative classifiers. WEKA 3.6.10 is used as a data mining tool for evaluations and results to be carried out.
Page(s)	:	92-98
ISSN	:	2319-7323
Source	:	Vol. 9, No. 1