Tech-Review of lung cancer prediction
Author(s):
Raghumanda Kavya Sree, Sai Prasad Ravi, Raveena Babu
Keywords:
Data mining, lung cancer prediction, Naïve Bayes , Deep Learning, machine Learning, ANN.
Abstract
Lung cancer is increasing at a rapid rate and due to the present pollution, it is increasing in non-smokers also. We really cannot predict anymore who will become the next victim of this deadly disease. But we can always take help from the technology. Early diagnosis of lung cancer can be very helpful in saving many lives, so the techniques to predict and detect early-stage cancer are increasing rapidly. Using generic lung cancer symptoms can help in predicting the possibility of getting lung cancer. In this study we list out few of the techniques involved in predicting lung cancer. Techniques such as Data Mining, Machine Learning, Automatic lung cancer prediction from X-ray images using Deep learning, are few of the technologies we will list out in this paper. In this paper we can see the use of classification based data mining techniques such as Naïve Bayes, Bayesian Network and Artificial Neural Network for the prediction of lung cancer. We also state how Machine Learning classifier techniques such as, Artificial Neural Network(ANN) and Support Vector Machine(SVM), are used to determine the data sets as cancerous and non-cancerous.
Article Details
Unique Paper ID: 151084

Publication Volume & Issue: Volume 7, Issue 11

Page(s): 575 - 583
Article Preview & Download


Share This Article

Conference Alert

ICM - STEP

International conference on Management, Science, Technology, Engineering, Pharmact and Humanities.

Latest Publication

Go To Issue



Call For Paper

Volume 7 Issue 9

Last Date 25 February 2020

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers