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Improving appendix cancer prediction with SHAP-based feature engineering for machine learning models: a prediction

¹Ewha Womans University College of Medicine, Seoul, Korea.

Ewha Medical Journal

|July 24, 2025

Summary

This summary is machine-generated.

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Shapley additive explanation (SHAP) feature engineering improved appendix cancer prediction accuracy and transparency. This interpretable model enhances clinical relevance for rare disease prediction.

Area of Science:

Oncology
Artificial Intelligence
Machine Learning

Background:

Traditional appendix cancer prediction models often lack transparency, limiting clinical adoption.
Interpretable AI is crucial for integrating predictive models into clinical practice.

Purpose of the Study:

To develop and evaluate a Shapley additive explanation (SHAP)-based feature engineering framework for appendix cancer prediction.
To enhance model accuracy and clinical interpretability by integrating SHAP for feature selection, construction, and weighting.

Main Methods:

Utilized the Kaggle Appendix Cancer Prediction dataset (260,000 samples, 21 features).
Applied data preprocessing including label encoding and SMOTE for class imbalance.
Compared baseline models (Random Forest, XGBoost, LightGBM), selecting LightGBM for superior performance.

Keywords:

Algorithms Appendiceal neoplasms Machine learning Random forest

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Implemented SHAP analysis for feature selection, interaction-based feature construction, and feature weighting.

Main Results:

The SHAP-based framework improved LightGBM model performance, with feature weighting achieving the highest F1-score (0.8877) and precision (0.9940).
Key predictive features identified included red blood cell count and chronic severity.
The engineered models maintained interpretability while enhancing predictive accuracy.

Conclusions:

The SHAP-based feature engineering framework significantly improved appendix cancer prediction accuracy and transparency.
This approach offers a scalable and interpretable solution for rare disease prediction.
Further validation with real-world data is recommended to ensure generalizability.