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使用机器学习算法开发和验证儿童和青少年胰岛素耐药性预测模型

Xiu Huang¹, Kun Yi¹, Lin Jia¹

¹Department of Paediatrics, Nanchong City Jialing District People's Hospital (Jialing Branch of Nanchong Central Hospital), Nanchong, China.

Translational pediatrics

|April 14, 2025

概括

机器学习使用体检数据准确预测儿童的胰岛素耐药性 (IR). 支持矢量机 (SVM) 模型显示了最高的准确性,有助于早期检测和预防代谢障碍.

科学领域:

儿科内分泌学儿科内分泌学
医疗保健中的机器学习
代谢健康代谢健康

背景情况:

胰岛素抵抗 (IR) 是严重代谢障碍的关键前体,如儿童群体的2型糖尿病和高血压.
早期发现IR对于预防严重的健康并发症至关重要.
现有的研究往往忽视了饮食因素,专注于有限的人口统计.

研究的目的:

评估机器学习 (ML) 模型,以准确预测儿童和青少年胰岛素抵抗 (IR) 的早期预测.
强调饮食因素在IR预测模型中的作用.
为了比较各种ML模型对IR检测的性能.

主要方法:

利用来自中国健康和营养调查 (CHNS) 数据库的体检数据进行培训,并使用医院数据进行验证.
使用恒温模式评估对IR (HOMA-IR) 评分 (>3.0表示IR) 评估IR.
与支持矢量机器 (SVM) 相比,详尽的千平方自动交互探测器 (E-CHAID),反向传播神经网络 (BPNN) 和后勤回归 (LR) 模型.

主要成果:

SVM模型实现了最高的预测准确性:91.90%在训练组和90.34%在测试组.
在所有指标上,SVM的表现优于E-CHAID,BPNN和LR模型,包括灵敏度,特异性和AUC.
根据体检数据,包括饮食因素,SVM模型在预测IR方面表现出卓越的性能.

结论:

与其他经过测试的ML模型相比,SVM模型在儿童和青少年中显示出对IR的优异预测能力.
SVM为早期临床预测IR提供了有价值的工具,有可能预防2型糖尿病及其相关并发症.
建议对不同种群进行进一步验证,以证实这些发现.

关键词:

胰岛素耐药性 (IR) 是一种青少年青少年青少年青少年孩子孩子孩子孩子孩子孩子机器学习 (ML) 是指机器学习.身体检查身体检查

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