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

ABSTRACT:

Purpose

This study aimed to leverage Shapley additive explanation (SHAP)-based feature engineering to predict appendix cancer. Traditional models often lack transparency, hindering clinical adoption. We propose a framework that integrates SHAP for feature selection, construction, and weighting to enhance accuracy and clinical relevance.

Methods

Data from the Kaggle Appendix Cancer Prediction dataset (260,000 samples, 21 features) were used in this prediction study conducted from January through March 2025, in accordance with TRIPOD-AI guidelines. Preprocessing involved label encoding, SMOTE (synthetic minority over-sampling technique) to address class imbalance, and an 80:20 train-test split. Baseline models (random forest, XGBoost, LightGBM) were compared; LightGBM was sele

SUBMITTER: Kim JY 

PROVIDER: S-EPMC12277501 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature