A Prognostic Machine Learning Framework and Algorithm for Predicting Long-term Behavioural Outcomes in Cancer Survivors

Anneliese Markus, Amos Roche, Chun-Kit Ngan, Yin-Ting Cheung, Kristi Prifti

2022

Abstract

We propose a prognostic machine learning (ML) framework to support the behavioural outcome prediction for cancer survivors. Specifically, our contributions are four-fold: (1) devise a data-driven, clinical domain-guided pipeline to select the best set of predictors among cancer treatments, chronic health conditions, and socio-environmental factors to perform behavioural outcome predictions; (2) use the state-of-the-art two-tier ensemble-based technique to select the best set of predictors for the downstream ML regressor constructions; (3) develop a StackNet Regressor Architecture (SRA) algorithm, i.e., an intelligent meta-modeling algorithm, to dynamically and automatically build an optimized multilayer ensemble-based RA from a given set of ML regressors to predict long-term behavioural outcomes; and (4) conduct a preliminarily experimental case study on our existing study data (i.e., 207 cancer survivors who suffered from either Osteogenic Sarcoma, Soft Tissue Sarcomas, or Acute Lymphoblastic Leukemia before the age of 18) collected by our investigators in a public hospital in Hong Kong. In this pilot study, we demonstrate that our approach outperforms the traditional statistical and computation methods, including Linear and non-Linear ML regressors.

Paper Citation

in Harvard Style

Markus A., Roche A., Ngan C., Cheung Y. and Prifti K. (2022). A Prognostic Machine Learning Framework and Algorithm for Predicting Long-term Behavioural Outcomes in Cancer Survivors. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 671-679. DOI: 10.5220/0010893700003123

in Bibtex Style

@conference{healthinf22,
author={Anneliese Markus and Amos Roche and Chun-Kit Ngan and Yin-Ting Cheung and Kristi Prifti},
title={A Prognostic Machine Learning Framework and Algorithm for Predicting Long-term Behavioural Outcomes in Cancer Survivors},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF},
year={2022},
pages={671-679},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010893700003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - A Prognostic Machine Learning Framework and Algorithm for Predicting Long-term Behavioural Outcomes in Cancer Survivors
SN - 978-989-758-552-4
AU - Markus A.
AU - Roche A.
AU - Ngan C.
AU - Cheung Y.
AU - Prifti K.
PY - 2022
SP - 671
EP - 679
DO - 10.5220/0010893700003123
PB - SciTePress