Enhancing Diagnostic Accuracy for Urinary Tract Disease through Explainable SHAP-Guided Feature Selection and Classification

TL;DR

This paper introduces a SHAP-guided feature selection method to improve the transparency and accuracy of machine learning models for diagnosing urinary tract diseases, especially bladder cancer.

Contribution

It presents a novel approach combining SHAP-based feature selection with ensemble classifiers to enhance diagnostic model interpretability and performance.

Findings

SHAP-guided feature selection maintains or improves model accuracy.

The approach enhances model transparency and reliability.

Effective for multiple binary classification scenarios.

Abstract

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.