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@article{ART002810438},
author={baikjaewook},
title={Machine learning in survival analysis},
journal={Industry Promotion Research},
issn={2466-1139},
year={2022},
volume={7},
number={1},
pages={1-8},
doi={10.21186/IPR.2022.7.1.001}

TY - JOUR
AU - baikjaewook
TI - Machine learning in survival analysis
JO - Industry Promotion Research
PY - 2022
VL - 7
IS - 1
PB - Industrial Promotion Institute
SP - 1
EP - 8
SN - 2466-1139
AB - We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of the features are independent variables. After applying various machine learning methods to the data, it has been found that just like many other reports from the artificial intelligence arena random forest performs better than logistic regression. But recently well performed artificial neural network and gradient boost do not perform as expected due to the lack of data. Finally Kaplan-Meier and Cox proportional hazard model have been employed to explore the relationship of the dependent variable (t_ｉ, δ_ｉ) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.
KW - survival data;machine learning;classification;survival analysis;random forest
DO - 10.21186/IPR.2022.7.1.001
ER -

baikjaewook. (2022). Machine learning in survival analysis. Industry Promotion Research, 7(1), 1-8.

baikjaewook. 2022, "Machine learning in survival analysis", Industry Promotion Research, vol.7, no.1 pp.1-8. Available from: doi:10.21186/IPR.2022.7.1.001

baikjaewook "Machine learning in survival analysis" Industry Promotion Research 7.1 pp.1-8 (2022) : 1.

baikjaewook. Machine learning in survival analysis. 2022; 7(1), 1-8. Available from: doi:10.21186/IPR.2022.7.1.001

baikjaewook. "Machine learning in survival analysis" Industry Promotion Research 7, no.1 (2022) : 1-8.doi: 10.21186/IPR.2022.7.1.001

baikjaewook. Machine learning in survival analysis. Industry Promotion Research, 7(1), 1-8. doi: 10.21186/IPR.2022.7.1.001

baikjaewook. Machine learning in survival analysis. Industry Promotion Research. 2022; 7(1) 1-8. doi: 10.21186/IPR.2022.7.1.001

baikjaewook. Machine learning in survival analysis. 2022; 7(1), 1-8. Available from: doi:10.21186/IPR.2022.7.1.001

baikjaewook. "Machine learning in survival analysis" Industry Promotion Research 7, no.1 (2022) : 1-8.doi: 10.21186/IPR.2022.7.1.001