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一个基于人口的对卷积神经网络的优化,用于慢性病预测
M Priyadharshini1, V Murugesh2, G V Samkumar2
1Department of Computer Science and Engineering, Faculty of Science and Technology (IcfaiTech), The ICFAI Foundation for Higher Education, Hyderabad, Telangana, 501203, India.
Scientific reports
|April 25, 2025
概括
与人口优化算法 (POA) 集成的新型深度神经网络 (DNN) OptiNet-CKD,在预测慢性病 (CKD) 中实现了100%的准确性. 这种强大的方法超越了改善CKD诊断的传统模型.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 慢性病 (CKD) 是全球健康面临的重大挑战,需要准确和及时的诊断工具.
- 传统的机器学习模型通常在复杂的医疗数据的复杂性,稳定性和准确性方面存在局限性.
- 对先进的预测模型的需求对于早期CKD检测和管理至关重要.
研究的目的:
- 介绍OptiNet-CKD,这是一种与人口优化算法 (POA) 集成的新型深度神经网络 (DNN) 模型,用于增强慢性病 (CKD) 预测.
- 解决现有的机器学习模型在CKD诊断的准确性,稳定性和概括性方面的局限性.
- 通过使用综合性指标,评估OptiNet-CKD与传统模型和基本DNN的性能.
主要方法:
- 预处理了一套CKD数据集,包括400个数字和分类特征的记录,包括对缺失数据的归算和用于DNN学习的特征缩放.
- 深度神经网络 (DNN) 与开发的人口优化算法 (POA) 集成,该算法利用初始化的网络群体和扰乱权重用于更广泛的解决方案空间探索.
- 使用关键性能指标评估了OptiNet-CKD模型:准确性,精度,回忆,F1得分和ROC AUC.
主要成果:
- 在所有评估指标上,OptiNet-CKD获得了完美的分数,包括100%准确性,1.0精度,1.0回忆,1.0F1分数和1.0ROC-AUC.
- 与传统模型 (逻辑回归,决策树) 和基本深度神经网络相比,该模型表现出卓越的性能和概括能力.
- 人口优化算法 (POA) 有效地避免了局部最小值,为模型的稳定性和预测准确性做出了贡献.
结论:
- OptiNet-CKD代表了慢性病 (CKD) 的可靠和稳健的预测方法,在概括和性能方面表现优于现有的方法.
- DNN和POA的组合为医学数据分析提供了一个有前途的策略,特别是在诊断诸如CKD之类的复杂疾病时.
- 该框架的简单性和有效性表明,它有可能扩展到具有类似数据复杂性的其他疾病,从而提高医疗保健中的诊断准确性.
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