在多学科医疗保健系统中早期检测乳腺癌的智能混合组合模型
Hasnain Iftikhar1,2, Atef F Hashem3, Moiz Qureshi2,4
1Department of Statistics, University of Peshawar, Peshawar 25120, Pakistan.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一个智能混合系统,结合机器学习 (ML),深度学习 (DL) 和整体方法,用于改进乳腺癌预测,优于现有方法.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 乳腺癌是全球女性死亡的主要原因之一.
- 准确的早期预测对于有效的治疗和改善生存率至关重要.
- 医疗数据的复杂性给高预测准确性带来了挑战.
研究的目的:
- 开发一种智能混合系统,用于增强乳腺癌预测.
- 整合传统的机器学习,深度学习和集体学习.
- 利用威斯康星州乳腺癌数据集用于模型开发和验证.
主要方法:
- 数据的预处理包括最小-最大规范化和合成少数过量采样技术 (SMOTE).
- 开发了多个机器学习算法,深度学习架构和一个新的合奏模型.
- 模型验证涉及三个培训测试场景和六个统计指标 (准确性,精度,回忆,F1分数,特异性,AUC).
主要成果:
- 与单个ML和DL模型相比,拟议的整体模型表现出卓越的预测准确性,稳定性和可靠性.
- 合奏系统的表现优于文学中的几种最先进的方法.
- 严格的统计测试证实了整体方法的预测一致性.
结论:
- 智能混合系统为乳腺癌诊断决策支持提供了一个有希望的方法.
- 集成先进的ML,DL和组合方法可以提高早期癌症检测.
- 这项工作有助于精确瘤学和人工智能驱动的医疗保健,以提供个性化的医疗保健.
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