Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148490
Title: Deep-learning in survival analysis
Authors: Ho, Jeff
Keywords: Science::Mathematics::Statistics
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Ho, J. (2021). Deep-learning in survival analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148490
Abstract: In this paper, hospitalisation duration is modelled using traditional survival model, machine-learning models and deep-learning models. Machine-learning and deep-learning algorithms typically assume that all event of interest are known at the time of modelling. However, hospitalisation duration is a time-to-event data with right-censoring (not all events are known at the time of modelling). Hence, specific techniques were employed to deal with this inconsistency. Subsequently, the various models are evaluated using the Concordance-index (C-index). It is a ranking evaluation metrics that can account for censored observation. The empirical results showed that the deep-learning model is best in predicting hospitalisation despite the small dataset. This paper can be further improved by incorporating geo-spatial data in the analysis.
URI: https://hdl.handle.net/10356/148490
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)

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