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Authors: Sonal Bajaj 1 and Waqar Haque 2

Affiliations: 1 Northern Health, Prince George, Canada ; 2 Department of Computer Science, University of Northern British Columbia, Prince George, Canada

ISBN: 978-989-758-398-8

ISSN: 2184-4305

Keyword(s): Data Modeling, Health Informatics, Oncology, Breast Cancer, Health Care Systems.

Abstract: A frequently asked question by cancer patients post-diagnosis is the lifespan they are left with. The oncologist’s response is generally based on past records of cancer patients with similar prognosis or by consulting other physicians and researchers working on comparable cases. Although careful prognosis is vital, it is difficult to predict accurate survival time of patients as survivability is based on many factors. Also, these predictions may not be accurate as the past records are not completely reliable and the prognosis from different oncologists are generally inconsistent. Further, existing repositories of data are not easily accessible and the stored formats are difficult to analyze. We propose an end-to-end process to build a model which predicts survival months of breast cancer patients. The predictive model is trained, tested and validated with different subsets of data. The modeling techniques used in this research are Neural Networks, CHAID, C&RT and an Ensemble of these techniques. The predictive model can also be used as a calculator which predicts survival months of a specific case. (More)

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Paper citation in several formats:
Bajaj, S. and Haque, W. (2020). Advanced Analytics to Predict Survivability of Breast Cancer Patients.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF, ISBN 978-989-758-398-8, ISSN 2184-4305, pages 295-302. DOI: 10.5220/0008857302950302

@conference{healthinf20,
author={Sonal Bajaj. and Waqar Haque.},
title={Advanced Analytics to Predict Survivability of Breast Cancer Patients},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF,},
year={2020},
pages={295-302},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008857302950302},
isbn={978-989-758-398-8},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF,
TI - Advanced Analytics to Predict Survivability of Breast Cancer Patients
SN - 978-989-758-398-8
AU - Bajaj, S.
AU - Haque, W.
PY - 2020
SP - 295
EP - 302
DO - 10.5220/0008857302950302

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