集成分类模型与基于CFS-IGWO的特征选择,用于使用微阵列数据检测癌症
Pinakshi Panda1, Sukant Kishoro Bisoy1, Sandeep Kautish2
1Department of Computer Science & Engineering, C. V. Raman Global University, Bidyanagar, Mahura, Janla 752054, Bhubaneswar, Odisha, India.
International journal of telemedicine and applications
|October 25, 2024
概括
机器学习有助于利用基因表达和微阵列数据早期发现癌症. 组合方法,特别是多数投票,在提高癌症预测的诊断准确性方面表现优越.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 癌症是全球主要的死亡原因,需要在早期检测方面取得进展.
- 机器学习 (ML) 为早期癌症诊断提供了有前途的方法,利用基因表达和微阵列数据.
- 在ML模型中的高维数据,在基因表达和微阵列数据集中很常见,可以降低效率.
研究的目的:
- 提出和评估两种组合技术,以改进基于ML的癌症诊断.
- 调查相关性特征选择 (CFS) 和改进的灰狼优化器 (IGWO) 在特征选择和优化方面的有效性.
- 为了比较多数投票和加权平均组合方法的性能.
主要方法:
- 用于ML模型训练的基因表达和微阵列数据.
- 应用相关性特征选择 (CFS) 用于特征选择和改进的灰狼优化器 (IGWO) 用于特征优化.
- 实施组合技术 (多数投票和加权平均) 来结合来自各种分类器的预测,包括SVM,MLP,LR,DT,AdaBoost,ELM和KNN.
主要成果:
- 使用精度 (ACC),特异性 (SPE),灵敏度 (SEN),精度 (PRE),马修斯相关系数 (MCC) 和F1得分 (F1-S) 评估模型性能.
- 大多数投票组合技术与加权平均组合技术相比,表现优越.
- 特性选择和优化方法 (CFS和IGWO) 对于处理高维数据至关重要.
结论:
- 组合方法,特别是多数投票,显著提高了基于ML的癌症诊断的准确性.
- 有效的特征选择和优化对于在癌症研究中管理高维的奥米克数据至关重要.
- 拟议的方法为早期癌症检测提供了一个强大的策略,可能降低死亡率.
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