Random Forest Modeling for Survival Analysis of Cancer Recurrences

Farhad Imani, Ruimin Chen, Conrad Tucker, Hui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The recurrence of breast cancer is a prevailing problem that decreases the quality of patients' lives, creates high burdens on the healthcare system, and impacts the wellbeing of society. Advanced sensing provides an unprecedented opportunity to increase information visibility and characterize patterns of event occurrences. However, few, if any, of previous works have investigated survival analysis of breast cancer recurrences based on large amount of data readily available in the health system. There is a dire need to leverage data to decipher important factors that play a role in the recurrence of breast cancer. This paper presents an ensemble method of random survival forest for time-to-event analysis of breast cancer recurrences in the surveillance, epidemiology, and end results (SEER) data from year 1973 to 2015. Our model characterizes the survival function among patients with and without recurrences of breast cancer. Ensemble models are constructed via sampling and bootstrapping into the big data. Experimental results show that the age when cancer recurrence happens and time-between-recurrences approximately follow the Gaussian and exponential distributions with the means of 61.35 \pm 14.03 and 2.61 years, respectively. In addition, the results show age, surgery status, stage of tumors, and histological grade are significant factors that influence the probability of breast cancer recurrences. The proposed survival analysis approach shows strong potentials to help healthcare practitioners in prognosis, treatment, and decision-making of breast cancer recurrences.

Original languageEnglish (US)
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages399-404
Number of pages6
ISBN (Electronic)9781728103556
DOIs
StatePublished - Aug 2019
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: Aug 22 2019Aug 26 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
CountryCanada
CityVancouver
Period8/22/198/26/19

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Random Forest Modeling for Survival Analysis of Cancer Recurrences'. Together they form a unique fingerprint.

Cite this