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http://dx.doi.org/10.21186/IPR.2022.7.1.001

Machine learning in survival analysis  

Baik, Jaiwook (Department of Statistics.Data Science, Korea National Open University)
Publication Information
Industry Promotion Research / v.7, no.1, 2022 , pp. 1-8 More about this Journal
Abstract
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 (ti, δi) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.
Keywords
survival data; machine learning; classification; survival analysis; random forest;
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