Machine Learning Techniques for Prediction of Lung Cancer
Nikita Banerjee1, Subhalaxmi Das2
1Nikita Banerjee*, Department of Computer Science and Engineering, Collage of Engineering and Technology, Bhubaneswar, India.
2Subhalaxmi Das, Department of Computer Science and Engineering, Collage of Engineering and Technology, Bhubaneswar, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 30, 2020. | PP: 241-249 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7124038620/2020©BEIESP | DOI: 10.35940/ijrte.F7124.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Lung cancer has been one of the deadliest diseases in today’s decades. It has become one of the causes of death in both man and woman. There are various reasons for which lung cancer occurs but classification of tumor and predicting it in the right stage is the most important part. This paper focused on the numerous approaches has been derived for lung cancer detection from different literature survey to advance the ability of detection of cancer. Digital image processing and data mining both are equally important because for prediction either image dataset or statistical dataset is used so for pre-processing the image dataset digital image processing is applied for statistical dataset data mining is applied. After pre-processing, segmentation and feature extraction we apply various machine learning algorithm for the prediction of lung cancer. So first we have provided a sketch of Machine learning and then various fields like in image data or statistical data where machine learning has been used for classification. Once the classification is done confusion matrix is generated for calculating accuracy, sensitivity, precision, these method is used to measure the rate of accuracy of the proposed model.
Keywords: Lung Cancer, Machine learning and its technique, Digital image processing.
Scope of the Article: Machine learning