使用KNN输入SMOTE特征和多模型合并学习方法改善宫癌的预测
Hanen Karamti1, Raed Alharthi2, Amira Al Anizi1
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Cancers
|September 9, 2023
概括
本研究介绍了使用机器学习检测子宫癌的自动化系统,通过KNN归算和SMOTE功能有效处理缺失数据,实现99.99%的准确性. 这种方法有助于早期识别和改善患者护理.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 宫癌是发展中国家女性死亡的主要原因之一.
- 早期检测和治疗对于尽量减少不良结果至关重要.
- 查图像分析是识别子宫癌的一个关键方法.
研究的目的:
- 开发一个用于宫癌预测的自动化系统.
- 为了应对数据集中缺失的值和类不平衡所带来的挑战.
- 提高用于宫癌检测的机器学习模型的准确性.
主要方法:
- 使用堆叠集体投票分类器模型.
- 集成的KNN Imputer用于处理缺失的数据.
- 采用SMOTE (合成少数群体过量采样技术) 来进行特征增量采样.
主要成果:
- 使用KNN归算的SMOTE功能实现了99.99%的准确性,精度,回忆和F1得分.
- 与删除缺失值或仅归算/SMOTE的模型相比,证明了优异的性能.
- 与现有的最先进的方法对拟议的模型进行了验证.
结论:
- 开发的系统有效地处理了宫癌检测数据中的缺失值和类不平衡.
- 这些发现可以帮助医疗从业者及时诊断和加强患者管理.
- 这种自动化方法有可能改善宫癌查和护理.
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