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机器学习驱动的预测维生素D缺乏的严重程度与混合优化

Usharani Bhimavarapu¹, Gopi Battineni^2,3, Nalini Chintalapudi²

¹Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India.

Bioengineering (Basel, Switzerland)

|February 26, 2025

概括

预测维生素D缺乏症 (VDD) 严重程度至关重要. 这项研究开发了一个机器学习模型,使用改进的鱼优化来准确,非侵入性检测VDD,达到99.4%的准确性.

科学领域:

生物医学信息学生物医学信息学
机器学习应用机器学习应用
营养科学营养科学

背景情况:

维生素D缺乏 (VDD) 构成全球健康风险.
标准的25-基维生素D (25-OH-D) 血液检测通常是不切实际的.
越来越需要对VDD严重程度进行非侵入性预测.

研究的目的:

开发一个临床上可接受的机器学习 (ML) 模型来检测VDD.
消除了对25-OH-D血液测试的需要.
解决地址模型过拟合并增强多类预测.

主要方法:

在维生素D数据集中应用了数据的减少,清理和转换.
利用改进的鱼优化 (IWOA) 算法进行特征选择和重量功能的优化.
使用堆叠分类器来提高预测准确度.

主要成果:

在VDD检测中达到99.4%的高精度.
拟议的IWOA算法在特征选择方面表现出卓越的性能.
与其他模型相比,堆叠分类器被证明是有效和强大的.

结论:

开发的ML模型为VDD评估提供了一个有希望的非侵入性方法.
先进的优化技术显著提高了预测准确度.
堆叠分类器是检测维生素D缺乏症的可靠选择.

关键词:

绩效指标绩效指标是一个指标.堆叠分类器堆叠分类器维生素D是维生素D的重要组成部分.鱼的优化优化鱼的优化

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