{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T03:04:48Z","timestamp":1785294288327,"version":"3.55.0"},"reference-count":11,"publisher":"The Ewha Medical Journal, Ewha Womans University College of Medicine","issue":"2","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/"}],"content-domain":{"domain":["e-emj.org"],"crossmark-restriction":true},"short-container-title":["Ewha Med J"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>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 selected for its superior performance (accuracy=0.8794). SHAP analysis identified key features and guided 3 engineering steps: selection of the top 15 features, construction of interaction-based features (e.g., chronic severity), and feature weighting based on SHAP values. Performance was evaluated using accuracy, precision, recall, and F1-score.Results: Four LightGBM model configurations were evaluated: baseline (accuracy=0.8794, F1-score=0.8691), feature selection (accuracy=0.8968, F1-score=0.8860), feature construction (accuracy=0.8980, F1-score=0.8872), and feature weighting (accuracy=0.8986, F1-score=0.8877). SHAP-based engineering yielded performance improvements, with feature weighting achieving the highest precision (0.9940). Key features (e.g., red blood cell count and chronic severity) contributed to predictions while maintaining interpretability.Conclusion: The SHAP-based framework substantially improved the accuracy and transparency of appendix cancer predictions using LightGBM (F1-score=0.8877). This approach bridges the gap between predictive power and clinical interpretability, offering a scalable model for rare disease prediction. Future validation with real-world data is recommended to ensure generalizability.<\/jats:p>","DOI":"10.12771\/emj.2025.00297","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T07:15:22Z","timestamp":1744701322000},"page":"e31","update-policy":"https:\/\/doi.org\/10.12771\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Improving appendix cancer prediction with SHAP-based feature engineering for machine learning models: a prediction study"],"prefix":"10.12771","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0830-8495","authenticated-orcid":true,"given":"Ji Yoon","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4998","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s11605-014-2726-7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.10589"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0316-z"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"key":"ref5","first-page":"359","volume-title":"What clinicians want: contextualizing explainable machine learning for clinical end use","author":"Tonekaboni","year":"2019"},{"key":"ref6","first-page":"4765","volume-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0304-0"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.17163"},{"key":"ref9","volume-title":"","year":"2020"},{"key":"ref10","first-page":"e1918660","volume-title":"Development and validation of a dynamic prediction model for unplanned hospital admission in cancer patients using electronic health records","author":"Banerjee","year":"2020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"}],"container-title":["Ewha Medical Journal"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/e-emj.org\/upload\/pdf\/emj-2025-00297.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/e-emj.org\/journal\/view.php?doi=10.12771\/emj.2025.00297","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/e-emj.org\/upload\/pdf\/emj-2025-00297.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T07:43:38Z","timestamp":1745826218000},"score":1,"resource":{"primary":{"URL":"http:\/\/e-emj.org\/journal\/view.php?doi=10.12771\/emj.2025.00297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,30]]},"references-count":11,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"URL":"https:\/\/doi.org\/10.12771\/emj.2025.00297","relation":{},"ISSN":["2234-3180","2234-2591"],"issn-type":[{"value":"2234-3180","type":"print"},{"value":"2234-2591","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,30]]},"assertion":[{"value":"2025-03-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-07","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-15","order":2,"name":"published","label":"Published online","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-30","order":3,"name":"published","label":"Print publication","group":{"name":"publication_history","label":"Publication History"}}]}}