A Study on the Risk Factors of Lung Cancer Using Machine Learning Methods

DOI

10.2991/978-94-6463-823-3_52How to use a DOI?

Keywords

Lung Cancer; Machine Learning; Parameter Adjusting

Abstract

Nowadays, lung cancer is widely present in many countries. The lung cancer has once again become the world’s leading cancer after being surpassed by breast cancer in 2020. Given the huge number of cases, the paper attempts to employ machine learning methods to explore whether there is a possibility of simplifying the process by which doctors detect the conditions of lung cancer patients and whether it is possible to enhance the detection ability of lung cancer using such methods. The dataset record 247samples of lung cancer patients and whether they are shown up in any of the 16 symptoms. This article uses two machine learning methods to model datasets. First, using logistic regression to model the dataset. Second, using boosting to test the learning performance. Aim to found out which machine learning model is more fitting the dataset. Subsequently, this article uses AUC & ROC curves to estimate the performance of learning models. Then, the article uses Mini-batch gradient decent to adjust the parameter. Last, using lose function to evaluate the model performance. The experimental results show that those machine learning methods can successfully model the dataset.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiahui Cui
PY  - 2025
DA  - 2025/08/31
TI  - A Study on the Risk Factors of Lung Cancer Using Machine Learning Methods
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 523
EP  - 531
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_52
DO  - 10.2991/978-94-6463-823-3_52
ID  - Cui2025
ER  -