Machine Learning for Colorectal Cancer Risk Prediction: Systematic Review

Noura Qarmiche, Mehdi Chrifi Alaoui, Nada Otmani, Samira El Fakir, Nabil Tachfouti, Hind Bourkhime, Mohammed Omari, Karima El Rhazi, Nour El Houda Chaoui

2021

Abstract

Colorectal cancer is one of the world's top five diseases and causes death from cancer. Survival is closely related to the stage at diagnosis and population-based screening reduces colorectal cancer incidence, and mortality. Machine learning algorithms have been used to develop risk prediction models in colorectal cancer. This study reported a systematic review of studies reporting the development of a machine learning model to predict the risk of colorectal cancer. We performed research on Scopus, Science direct, and web of science Library. We included original articles reporting or validating machine learning models predicting colorectal cancer risk, published between 2015 and 2021. We identified nine articles related to eleven distinct models; three models considered genetic factors only; two models required clinical assessment; the remaining models are based on nutrition, demographic and lifestyle features. Models were validated by computing accuracy, sensitivity and air under the roc curve. The most used algorithms are neural networks and logistic regression. Machine learning models have shown promising performance for colorectal cancer risk prediction. However, they need to be improved for easy and safe clinical practice use.

Paper Citation

in Harvard Style

Qarmiche N., Chrifi Alaoui M., Otmani N., El Fakir S., Tachfouti N., Bourkhime H., Omari M., El Rhazi K. and Chaoui N. (2021). Machine Learning for Colorectal Cancer Risk Prediction: Systematic Review. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 507-511. DOI: 10.5220/0010738100003101

in Bibtex Style

@conference{bml21,
author={Noura Qarmiche and Mehdi Chrifi Alaoui and Nada Otmani and Samira El Fakir and Nabil Tachfouti and Hind Bourkhime and Mohammed Omari and Karima El Rhazi and Nour El Houda Chaoui},
title={Machine Learning for Colorectal Cancer Risk Prediction: Systematic Review},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={507-511},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010738100003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Machine Learning for Colorectal Cancer Risk Prediction: Systematic Review
SN - 978-989-758-559-3
AU - Qarmiche N.
AU - Chrifi Alaoui M.
AU - Otmani N.
AU - El Fakir S.
AU - Tachfouti N.
AU - Bourkhime H.
AU - Omari M.
AU - El Rhazi K.
AU - Chaoui N.
PY - 2021
SP - 507
EP - 511
DO - 10.5220/0010738100003101