混合模型用于精确的C型肝炎分类,使用改进的随机森林和SVM方法
Umesh Kumar Lilhore1, Poongodi Manoharan2, Jasminder Kaur Sandhu1
1Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India.
Scientific reports
|August 1, 2023
概括
这项研究引入了用于检测C型肝炎病毒 (HCV) 的混合预测模型 (HPM),显著提高了准确性. HPM有效地解决了数据不平衡和过度匹配,这对于可靠的HCV诊断至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
背景情况:
- 肝炎C病毒 (HCV) 感染导致肝炎,全球每年有数百万病例报告.
- 早期诊断HCV对于有效治疗和改善患者的治疗结果至关重要.
- 现有的用于HCV预测的机器学习 (ML) 模型存在诸如精度差和数据不平衡等局限性.
研究的目的:
- 开发和评估一种新的混合预测模型 (HPM) 用于C型肝炎病毒 (HCV) 预测.
- 在准确性和数据不平衡方面克服现有的单一ML模型的局限性.
- 通过特征选择和先进技术,提高ML模型在HCV诊断中的性能.
主要方法:
- 提出了一个混合预测模型 (HPM),将改进的随机森林 (IRF) 与支持向量机 (SVM) 集成.
- 增强了随机森林算法,使用启动方法来代地消除小特征.
- 使用"排名方法"来选择特征,以及合成少数人过量采样技术 (SMOTE) 来解决数据集不平衡.
主要成果:
- 该HPM实现了高准确率,包括96.29%的十倍交叉验证和92.39%的70:30列车测试分割.
- 实验2显示,使用基于SMOTE的特征选择,精度从41.54%提高到96.82%.
- 拟议的HPM在准确性方面超过了SVM,MARS,RF,DT和BGLM等现有方法.
结论:
- 混合预测模型 (HPM) 为C型肝炎病毒 (HCV) 预测提供了强大而准确的解决方案.
- 像SMOTE这样的特征选择和技术对于改善HCV研究不平衡数据集中的ML模型的性能至关重要.
- 该研究强调了先进的ML方法在提高早期HCV诊断和管理方面的潜力.
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