Application of Machine Learning for Prediction of Lung Cancer using Omics Data
Arwinder Dhillon1, Amrita Kaur2, Ashima Singh3
1Arwinder Dhillon*, Assistant Professor, Computer Science and Engineering Department, at Thapar Institute of Engineering & Technology, Patiala.
2Amrita Kaur, Research Scholar in Computer Science and Engineering Department, at Thapar Institute of Engineering & Technology, Patiala.
3Ashima Singh, Leturer in Computer Science and Engineering Department, at Thapar Institute of Engineering & Technology, Patiala
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on April 10, 2020. | PP: 230-236 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3652049620/2020©BEIESP | DOI: 10.35940/ijitee.F3625.049620
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© 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: Cancer is one of the deadly diseases across many countries. However, cancer can be cured, if detected at an early stage. Researchers are working on healthcare for early detection and prevention of cancer. Medical data has reached its utmost potential by providing researchers with huge data sets collected from all over the globe. In the present scenario, Machine Learning has been widely used in the area of cancer diagnosis and prognosis. Survival analysis may help in the prediction of the early onset of disease, relapse, re-occurrence of diseases and biomarker identification. Applications of machine learning and data mining methods in medical field are currently the most widespread in cancer detection and survival analysis. In this survey, different ways to detect and predict lung cancer using latest Machine learning algorithms combined with data mining has been analyzed. Comparative study of various machine learning techniques and technologies has been done over different types of data such as clinical data, omics data, image data etc.
Keywords: Lung Cancer, Omics Data, Images data, Machine Learning, Survival Analysis, Supervised Learning
Scope of the Article: Machine Learning (ML) and Knowledge Mining (KM)