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Lung cancer risk prediction using augmented machine learning pipelines with explainable AI.

Pavithran M S¹, Saranyaraj D¹, Anirban Chakrabortty¹

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Tamil Nadu, India.

Frontiers in Artificial Intelligence

|September 19, 2025

Summary

Machine learning models for lung cancer prediction struggle with imbalanced data. K-Means SMOTE augmentation with a Multi-Layer Perceptron classifier achieved 93.55% accuracy, improving lung cancer risk prediction.

Keywords:

SMOTE class imbalance explainable AI lime lung cancer prediction

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Area of Science:

Oncology
Medical Informatics
Computer Science

Background:

Lung cancer is a leading cause of cancer mortality globally, necessitating improved diagnostic accuracy.
Machine learning (ML) shows potential for lung cancer prediction, but clinical datasets often suffer from class imbalance, hindering classifier performance.
Class imbalance leads to biased predictions and reduced accuracy in ML models for medical diagnosis.

Purpose of the Study:

To evaluate the effectiveness of various data augmentation techniques in addressing class imbalance for lung cancer prediction using ML.
To compare the performance of different augmentation-classifier combinations on a small, imbalanced lung cancer dataset.
To enhance the accuracy and reliability of ML-based lung cancer risk prediction models.

Main Methods:

Applied several data augmentation techniques to a small lung cancer dataset characterized by significant class imbalance.
Trained and evaluated various ML classifiers in combination with these augmentation methods.
Utilized K-Means SMOTE combined with a Multi-Layer Perceptron (MLP) classifier for comparative analysis.
Employed LIME (Local Interpretable Model-agnostic Explanations) for model interpretability.

Main Results:

The combination of K-Means SMOTE data augmentation and a Multi-Layer Perceptron classifier yielded the highest performance.
Achieved a classification accuracy of 93.55% and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) score of 96.76%.
This augmentation-classifier pair significantly outperformed other combinations tested, demonstrating the impact of optimized data augmentation.

Conclusions:

Data augmentation techniques are crucial for mitigating class imbalance issues in ML-based medical prediction tasks.
The K-Means SMOTE and MLP combination represents a promising approach for improving lung cancer risk prediction accuracy.
Further validation with larger, more representative datasets is warranted to translate these findings into clinical tools.