RFFE - 随机森林模糊透用于糖尿病类别
A Usha Ruby1, J George Chellin Chandran1, T J Swasthika Jain2
1School of Computing Science and Engineering Department, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, Madhya Pradesh-466114, India.
AIMS public health
|June 12, 2023
概括
早期发现糖尿病对于管理这种慢性疾病至关重要. 一个新的随机森林模糊透模型准确地预测糖尿病风险,在皮马印度糖尿病数据集上达到98%的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 计算生物学 计算生物学
背景情况:
- 糖尿病是一种慢性代谢疾病,因胰岛素生产不足而导致血糖水平升高.
- 糖尿病的并发症会影响视网膜,脏和神经等重要器官,需要终身治疗.
- 早期检测和风险评估对于预防糖尿病及其相关健康问题至关重要.
研究的目的:
- 使用机器学习开发和评估一种用于早期糖尿病预测的新型原型.
- 通过将 Fuzzy Entropy 与随机森林算法集成来提高糖尿病预测的准确性.
- 将拟议模型的性能与各种既定机器学习技术进行比较.
主要方法:
- 该研究采用了一种包含数据归算,采样和特征选择的原型.
- 关键的预测技术包括模糊的透,合成少数群体过量采样技术 (SMOTE),卷积神经网络 (CNN) 与随机梯度下降与动量 (SGDM),支持向量机器 (SVM),分类和回归树 (CART),K-最近邻居 (KNN) 和天真贝耶斯 (NB).
- 皮马印度糖尿病 (PID) 数据集被用于模型培训和验证,通过混矩阵和ROCAUC.评估性能.
主要成果:
- 拟议的随机森林模糊 (RFFE) 模型在糖尿病预测方面表现出卓越的表现.
- 在PID数据集上,RFFE模型实现了98%的高准确率.
- 对比分析证实了RFFE与其他测试过的机器学习算法的有效性.
结论:
- 随机森林模糊 (RFFE) 方法是早期糖尿病预测的高效和有价值的工具.
- 通过先进的机器学习模型进行准确的早期检测,可以显著帮助糖尿病预防和管理策略.
- 这些发现突出了将模糊透与整体方法集成的潜力,以改进慢性疾病风险评估.
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