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Machine learning in survival analysis

  • Industry Promotion Research
  • Abbr : IPR
  • 2022, 7(1), pp.1-8
  • DOI : 10.21186/IPR.2022.7.1.001
  • Publisher : Industrial Promotion Institute
  • Research Area : Interdisciplinary Studies > Interdisciplinary Research
  • Received : January 4, 2022
  • Accepted : January 20, 2022
  • Published : January 31, 2022

baikjaewook 1

1한국방송통신대학교

Accredited

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 (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.

Citation status

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