乳腺癌仍然是全世界女性死亡的主要原因，由于认识有限、筛查资源和治疗选择不足，乳腺癌的情况进一步恶化。准确和早期诊断对于提高生存率和有效治疗至关重要。本研究旨在开发一种基于创新人工智能（AI）的模型，通过整合多种生物标志物和受试者年龄来预测乳腺癌及其各种组织病理学分级，从而提高诊断准确性和治疗效果。引入了一种新颖的基于集成的机器学习 (ML) 框架，该框架集成了三种不同的生物标志物 - β-人绒毛膜促性腺激素 (β-hCG)、程序性细胞死亡配体 1 (PD-L1) 和甲胎蛋白 (AFP) )-连同受试者年龄。使用粒子群优化（PSO）算法进行超参数优化，并采用少数过采样技术来减轻过度拟合。该模型的性能通过严格的五重交叉验证进行了验证。所提出的模型表现出了卓越的性能，在不同年龄组的精心标记的测试数据上实现了 97.93% 的准确率和 98.06% 的 F1 分数。比较分析表明，该模型优于最先进的方法，凸显了其稳健性和普遍性。通过对多种生物标志物进行全面分析并有效预测肿瘤分级，这项研究在乳腺癌筛查方面取得了重大进展，特别是在乳腺癌筛查领域在医疗资源有限的情况下。拟议的框架有可能降低乳腺癌死亡率并改善早期干预和个性化治疗策略。版权所有 © 2024 Elsevier B.V. 保留所有权利。

Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment.This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication.A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation.The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability.By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.Copyright © 2024 Elsevier B.V. All rights reserved.