INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
DIAGNOSIS OF STARTING STAGE LUNG CANCER DETECTION USING MACHINE LEARNING
Authors Name:
George Romand
Unique Id:
IJSDR1912044
Published In:
Volume 4 Issue 12, December-2019
Abstract:
The early detection can be useful in curing the sickness completely. So the obligation of technique to notice the happening of development protuberance in early juncture is escalating. A contamination that is commonly misdiagnosed is lung cancer. Earlier diagnosis of Lung Cancer saves enormous lives, failing which may lead to other severe problems causing unexpected mortal end. Its cure rate and prediction depends for the most part on the early detection and diagnosis of the disease. One of the most common forms of medical malpractice globally is an error in diagnosis. Knowledge discovery and data mining have found numerous applications in commerce and scientific domain. Valuable knowledge can be discovered from capitulation of data taking away techniques in healthcare system. In work, briefly examine the potential use of classification based data mining techniques such as Rule based, Decision tree, Naïve Bayes and fake Neural Network to massive volume.
Keywords:
Diagnosis Of Lung Cancer ,Classification Techniques, Neural Network, ,Disease
Cite Article:
"DIAGNOSIS OF STARTING STAGE LUNG CANCER DETECTION USING MACHINE LEARNING", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.4, Issue 12, page no.214 - 222, December-2019, Available :http://www.ijsdr.org/papers/IJSDR1912044.pdf
Downloads:
000337067
Publication Details:
Published Paper ID: IJSDR1912044
Registration ID:191180
Published In: Volume 4 Issue 12, December-2019
DOI (Digital Object Identifier):
Page No: 214 - 222
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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