TY - GEN
T1 - Explore machine learning for analysis and prediction of lung cancer related risk factors
AU - Tang, Haijing
AU - Zhao, Jing
AU - Yang, Xu
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/12/8
Y1 - 2018/12/8
N2 - Lung cancer has become a major disease that endangers the life and health of people all over the world. Many researchers have carried out research on the prevention and treatment of lung cancer. In this paper, we adopted machine learning methods to analyze lung cancer related risk factors, and try to find a way to predict it, based on research of medical follow-up data. The data used in this paper are from a large prospective Chinese chronic disease research project (China Kadoorie Biobank,CKB), initiated by the China Centers for Disease Control and Prevention in collaboration with the University of Oxford, UK. The data is the largest in the Chinese cohort study, the most widely related data (including age, occupation, region, environment, etc.), and the most reliable sample of local cohort data collected.
AB - Lung cancer has become a major disease that endangers the life and health of people all over the world. Many researchers have carried out research on the prevention and treatment of lung cancer. In this paper, we adopted machine learning methods to analyze lung cancer related risk factors, and try to find a way to predict it, based on research of medical follow-up data. The data used in this paper are from a large prospective Chinese chronic disease research project (China Kadoorie Biobank,CKB), initiated by the China Centers for Disease Control and Prevention in collaboration with the University of Oxford, UK. The data is the largest in the Chinese cohort study, the most widely related data (including age, occupation, region, environment, etc.), and the most reliable sample of local cohort data collected.
KW - Disease prediction
KW - Follow-up data
KW - Lung cancer
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85062786621&partnerID=8YFLogxK
U2 - 10.1145/3297156.3297274
DO - 10.1145/3297156.3297274
M3 - Conference contribution
AN - SCOPUS:85062786621
T3 - ACM International Conference Proceeding Series
SP - 41
EP - 45
BT - Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, CSAI 2018 - 2018 the 10th International Conference on Information and Multimedia Technology, ICIMT 2018
PB - Association for Computing Machinery
T2 - 2nd International Conference on Computer Science and Artificial Intelligence, CSAI 2018
Y2 - 8 December 2018 through 10 December 2018
ER -
