The Advancements of Machine Learning Applications in Cancer Research and Treatment

Xinze Li

2024

Abstract

The cancer, also known as the malignant tumor, is a disease caused by the abnormal cell proliferation. It ranked sixth in the top 10 causes of death published by the World Trade Organization in 2019, and seriously threatens human life and health. The mortality rate is 93% and 19.3 million people developed cancer in 2022 alone. This paper discusses some well-established machine learning-based cancer treatment research methods, including the application of simple models like random forests and some complex models such as neural networks in breast cancer, lung cancer and thyroid cancer. In addition, most of the models in this article are deep learning models because their application scope and value are generally higher than traditional models. For each cancer mentioned, two or three models are presented, along with their basic information and their results. This review can provide some references for the application of machine learning in cancer treatment research.

Paper Citation

in Harvard Style

Li X. (2024). The Advancements of Machine Learning Applications in Cancer Research and Treatment. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 101-105. DOI: 10.5220/0013509400004619

in Bibtex Style

@conference{daml24,
author={Xinze Li},
title={The Advancements of Machine Learning Applications in Cancer Research and Treatment},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={101-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013509400004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - The Advancements of Machine Learning Applications in Cancer Research and Treatment
SN - 978-989-758-754-2
AU - Li X.
PY - 2024
SP - 101
EP - 105
DO - 10.5220/0013509400004619
PB - SciTePress