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Using Machine Learning Techniques for Lung Cancer Survival Prediction.

Rodrigo Henrich^1,2, Rafael H Bordini¹, Isabel H Manssour¹

¹Pontifical Catholic University of Rio Grande do Sul, PUCRS.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

This summary is machine-generated.

This study developed an artificial intelligence model to predict lung cancer patient survival time, achieving 84.6% accuracy in classifying long or short survival. The model enhances clinical decision-making for personalized lung cancer treatment.

Keywords:

SHAP Survival prediction cancer lung machine learning

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Area of Science:

Oncology
Medical Informatics
Artificial Intelligence in Medicine

Background:

Lung cancer remains a leading cause of cancer-related mortality globally.
Timely diagnosis and tailored treatment are critical for improving patient outcomes.
Artificial intelligence (AI) offers potential for enhancing clinical decision support in oncology.

Purpose of the Study:

To develop and evaluate a machine learning model for predicting lung cancer patient survival.
To classify patient survival into 'long' or 'short' timeframes.
To enhance the interpretability of the prediction model using SHAP values.

Main Methods:

Application of machine learning techniques to a dataset of lung cancer patients.
Development of a predictive model for survival time classification.
Utilized SHAP (SHapley Additive exPlanations) for model explainability.

Main Results:

The developed machine learning model achieved an accuracy of 84.6% in predicting survival classification.
SHAP analysis identified key variables influencing the survival time predictions.
The model demonstrated potential for supporting clinical decisions in lung cancer care.

Conclusions:

Machine learning models can effectively predict lung cancer patient survival.
Explainable AI (SHAP) improves trust and understanding of predictive models in clinical settings.
This approach can aid in personalized treatment strategies and improved patient management for lung cancer.