Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability

2024-03-14

Inteligencia Artif.

AI Summary

This work addresses the high false-negative rate and insufficient model interpretability in early breast cancer screening. Methodologically, it proposes a unified modeling framework integrating hyperparameter optimization with explainable AI: (1) for the first time, AdaBoost, XGBoost, CatBoost, and LightGBM are jointly optimized within a single Optuna framework; (2) SHAP is incorporated for feature-level attribution analysis to ensure clinical interpretability; and (3) recall and ROC-AUC are adopted as primary optimization objectives to explicitly minimize missed diagnoses. Experimental results demonstrate that all models achieve AUC ≥ 99.41%; notably, AdaBoost and LightGBM exhibit substantial reductions in false-negative rate, with both recall and AUC consistently surpassing those of conventional single-model hyperparameter tuning. This framework advances reliable, trustworthy AI-assisted diagnosis for breast cancer by unifying robust performance with clinical transparency.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection and, consequently, early treatment. Any development for detection or perdition this kind of cancer is important for a better healthy life. Many studies focus on a model with high accuracy in cancer prediction, but sometimes accuracy alone may not always be a reliable metric. This study implies an investigative approach to studying the performance of different machine learning algorithms based on boosting to predict breast cancer focusing on the recall metric. Boosting machine learning algorithms has been proven to be an effective tool for detecting medical diseases. The dataset of the University of California, Irvine (UCI) repository has been utilized to train and test the model classifier that contains their attributes. The main objective of this study is to use state-of-the-art boosting algorithms such as AdaBoost, XGBoost, CatBoost and LightGBM to predict and diagnose breast cancer and to find the most effective metric regarding recall, ROC-AUC, and confusion matrix. Furthermore, previous studies have applied Optuna to individual algorithms like XGBoost or LightGBM, but no prior research has collectively examined all four boosting algorithms within a unified Optuna framework, a library for hyperparameter optimization, and the SHAP method to improve the interpretability of our model, which can be used as a support to identify and predict breast cancer. We were able to improve AUC or recall for all themodels and reduce the False Negative for AdaBoost and LigthGBM the final AUC were more than 99.41% for all models.

Problem

• Reducing false negatives in breast cancer detection using boosting algorithms
• Optimizing recall and AUC metrics for accurate breast cancer diagnosis
• Enhancing model interpretability with SHAP and hyperparameter tuning

Innovation

• Uses boosting algorithms for breast cancer prediction
• Optimizes hyperparameters with Optuna library
• Enhances interpretability via SHAP method