现代电子技术2026，Vol.49Issue(20)：59-64,6.DOI:10.16652/j.issn.1004-373X.2026.20.010
融合改进河马优化算法与Stacking集成的乳腺癌诊断模型
Breast cancer diagnostic model integrating IHO algorithm and Stacking ensemble
摘要
Abstract
Breast cancer is a major disease that seriously threatens people's lives and health,and its early and accurate diagnosis is the key to improving patients'survival rates.Traditional machine learning models exhibit inefficiencies in hyperparameter tuning and are prone to local optima,resulting in slow convergence speed,low optimization accuracy and poor robustness.The insufficient optimization of base models in ensemble learning methods makes it difficult to fully exploit the complementary advantages among models.To address these challenges,a Stacking ensemble model(IHO-Stacking)integrating improved hippopotamus optimization(IHO)algorithm is proposed.The population initialization mechanism is reconstructed by means of Latin hypercube sampling,and the hippopotamus optimization algorithm is improved by combining with the Lévy flight strategy and Gaussian mutation mechanism.The IHO algorithm is used to globally optimize the hyperparameters of base learners including extreme gradient boosting(XGBoost),categorical boosting(CatBoost)and random forest(RF),and integrate the advantages of multiple models by means of the Stacking framework.The experimental results show that the IHO-Stacking model can realize outstanding performance on the Wisconsin breast cancer dataset,with an accuracy of 0.985 4,malignant sensitivity improved to 0.986 2,and benign specificity increased to 0.990 7.It significantly outperforms traditional optimization methods and single models,and can provide strong technical support for the precision and standardization of early breast cancer screening.关键词
乳腺癌诊断/改进河马优化算法/Stacking集成学习/超参数优化/莱维飞行策略/高斯变异机制Key words
breast cancer diagnosis/improved hippopotamus optimization algorithm/Stacking ensemble learning/hyperparameter optimization/Lévy flight strategy/Gaussian mutation mechanism分类
信息技术与安全科学引用本文复制引用
董旭泽,赵晗,杨爱民,袁书娟,孔闪闪..融合改进河马优化算法与Stacking集成的乳腺癌诊断模型[J].现代电子技术,2026,49(20):59-64,6.基金项目
中央引导地方科技发展资金项目(自由探索类基础研究):调节性细胞死亡在纳米氧化铟锡诱导的肺损伤中的作用及机制研究(246Z7734G) （自由探索类基础研究）
