Random forest classification of four unsaturated fatty acids for infection diagnosis and prognosis in female Chinese patients.

IF 1.6 Q2 MEDICINE, GENERAL & INTERNAL
Annals of Medicine and Surgery Pub Date : 2025-07-22 eCollection Date: 2025-08-01 DOI:10.1097/MS9.0000000000003570
Shuhua Tong, ShiShi Wu, Jiaying Wu, Siyu Zhuo, Jin Ruyi, Qian Jingjing, Peng Zhou, Li Wang, Lufeng Hu, Xinjie Zhu
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Abstract

Objective: Unsaturated fatty acids (UFAs) are important in immune regulation and inflammation; however, their diagnostic and prognostic value in infected patients is unclear. This study evaluated UFAs in infected female patients versus healthy controls using random forest (RF) analysis.

Methods: A total of 115 female subjects, consisting of healthy controls and infected patients, were recruited. Clinical indices and UFAs, arachidonic acid (AA), docosahexaenoic acid (DHA), linoleic acid, and alpha-linolenic acid (ALA), and complete blood cell (CBC) count data were analyzed. Correlation and regression analyses were conducted to assess the relationships between UFAs in healthy and infected states. Receiver operating characteristic (ROC) analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) modeling were performed to evaluate the diagnostic value of UFAs. An RF model was developed to classify infected and healthy populations.

Results: Significant differences were observed in CBC and UFA indices between healthy and infected female patients. The ROC analysis demonstrated that UFAs showed statistically significant differences, although these indices alone did not completely discriminate between infected and healthy individuals. However, the RF model, incorporating both UFAs and CBC data, achieved a clear separation between the infected and healthy groups, outperforming the OPLS-DA model. The ROC results for both UFAs and CBC datasets indicated predictive value for infection prognosis.

Conclusion: The integration of UFAs and CBC data within an RF model provides enhanced diagnostic and prognostic accuracy compared to OPLS-DA. UFAs, particularly AA and ALA, possess substantial predictive value for infection prognosis.

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四种不饱和脂肪酸的随机森林分类对中国女性感染诊断和预后的影响。
目的:不饱和脂肪酸(UFAs)在免疫调节和炎症反应中具有重要作用;然而,它们在感染患者中的诊断和预后价值尚不清楚。本研究使用随机森林(RF)分析评估感染女性患者与健康对照者的UFAs。方法:共招募115名女性受试者,包括健康对照和感染患者。分析临床指标及UFAs、花生四烯酸(AA)、二十二碳六烯酸(DHA)、亚油酸、α -亚麻酸(ALA)、全血细胞计数数据。通过相关和回归分析来评估健康和感染状态下UFAs之间的关系。采用受试者工作特征(ROC)分析和正交偏最小二乘判别分析(OPLS-DA)模型评价UFAs的诊断价值。建立了一个射频模型来对感染人群和健康人群进行分类。结果:健康女性患者与感染女性患者的CBC和UFA指标存在显著差异。ROC分析表明,UFAs在统计学上有显著差异,尽管仅凭这些指标并不能完全区分感染个体和健康个体。然而,结合UFAs和CBC数据的RF模型在感染组和健康组之间实现了明确的分离,优于OPLS-DA模型。UFAs和CBC数据集的ROC结果显示了感染预后的预测价值。结论:与OPLS-DA相比,RF模型中UFAs和CBC数据的整合提供了更高的诊断和预后准确性。UFAs,尤其是AA和ALA对感染预后具有重要的预测价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of Medicine and Surgery
Annals of Medicine and Surgery MEDICINE, GENERAL & INTERNAL-
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5.90%
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1665
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