用可解释性方法提升膀胱癌诊断准确率
Enhancing Diagnostic Accuracy for Urinary Tract Disease through Explainable SHAP-Guided Feature Selection and Classification
- 基于SHAP值筛选关键特征，增强模型可解释性
- 在多个分类任务中保持并提升准确率与特异性
- 适合临床决策支持系统开发者参考
本文提出一种支持泌尿系统疾病诊断的方法，重点针对膀胱癌，采用基于SHAP（SHapley Additive exPlanations）的特征选择来提升预测模型的透明性与有效性。构建了六个二分类场景，用于区分膀胱癌与其他泌尿及肿瘤性疾病。使用XGBoost、LightGBM和CatBoost算法，并通过Optuna进行超参数优化，利用SMOTE技术处理类别不平衡问题。通过SHAP重要性值指导特征选择，在保持或提升平衡准确率、精确率和特异性等性能指标的同时实现模型可解释性增强。实验证明，结合可解释性技术进行特征选择是有效策略。该方法有助于开发更透明、可靠且高效的临床决策支持系统，优化泌尿系统疾病的筛查与早期诊断。
原文摘要 · Abstract (English)
In this paper, we propose an approach to support the diagnosis of urinary tract diseases, with a focus on bladder cancer, using SHAP (SHapley Additive exPlanations)-based feature selection to enhance the transparency and effectiveness of predictive models. Six binary classification scenarios were developed to distinguish bladder cancer from other urological and oncological conditions. The algorithms XGBoost, LightGBM, and CatBoost were employed, with hyperparameter optimization performed using Optuna and class balancing with the SMOTE technique. The selection of predictive variables was guided by importance values through SHAP-based feature selection while maintaining or even improving performance metrics such as balanced accuracy, precision, and specificity. The use of explainability techniques (SHAP) for feature selection proved to be an effective approach. The proposed methodology may contribute to the development of more transparent, reliable, and efficient clinical decision support systems, optimizing screening and early diagnosis of urinary tract diseases.
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