Enhanced Lung Cancer Severity Prediction Based on Random Forest Models: A Comprehensive Analysis of Predictive Accuracy and Feature Importance

DOI

10.2991/978-94-6463-540-9_3How to use a DOI?

Keywords

Lung Cancer Prediction; Machine learning; Random Forest Model

Abstract

Precise forecasting of lung cancer is crucial due to its high mortality rate. Integrating Artificial Intelligence (AI) into medical diagnostics offers significant potential to enhance prediction accuracy and early detection. This study utilized a dataset from Kaggle, consisting of approximately 1000 records with diverse features including age, smoking status, genetic risks, and environmental pollutant exposure. A random forest model was employed to classify lung cancer severity into three categories: low, moderate, and high. The model achieved an outstanding accuracy rate of 100%, underscoring its predictive capability. Key features such as ‘Coughing of Blood,’ ‘Passive Smoker,’ and ‘Obesity’ were identified as the most significant contributors to the model’s predictions. This feature importance analysis provided valuable insights into the critical factors influencing lung cancer severity. In conclusion, this research introduces a highly accurate and interpretable predictive model for lung cancer severity. The model not only achieves exceptional precision but also offers insights into key predictive factors, enhancing its reliability and utility in clinical practice. Future studies are suggested to focus on validating this model across diverse populations and explore the integration of additional machine learning techniques to further refine its predictive power.

Copyright

© 2024 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  - Yanming Zhang
PY  - 2024
DA  - 2024/10/16
TI  - Enhanced Lung Cancer Severity Prediction Based on Random Forest Models: A Comprehensive Analysis of Predictive Accuracy and Feature Importance
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 18
EP  - 26
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_3
DO  - 10.2991/978-94-6463-540-9_3
ID  - Zhang2024
ER  -