Accurate detection of chronic kidney disease (CKD) plays a pivotal role in early diagnosis and treatment. Measured glomerular filtration rate (mGFR) is considered the benchmark indicator in measuring the kidney function. However, due to the high resource cost of measuring mGFR, it is usually approximated by the estimated glomerular filtration rate, underscoring an urgent need for more precise and stable approaches. With the introduction of novel machine learning methodologies, prediction performance is shown to be significantly improved across all available data, but the performance is still limited because of the lack of models in dealing with ultra-high dimensional datasets. This study aims to provide a two-stage neural network approach for prediction of GFR and to suggest some other useful biomarkers obtained from the blood metabolites in measuring GFR. It is a composite of feature shrinkage and neural network when the number of features is much larger than the number of training samples. The results show that the proposed method outperforms the existing ones, such as convolutionneural network and direct deep neural network.

中文翻译：

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慢性肾病的两阶段神经网络预测。

慢性肾脏病（CKD）的准确检测在早期诊断和治疗中起着关键作用。测得的肾小球滤过率 (mGFR) 被认为是衡量肾功能的基准指标。然而，由于测量 mGFR 的资源成本很高，它通常用估计的肾小球滤过率来近似，强调迫切需要更精确和稳定的方法。随着新机器学习方法的引入，所有可用数据的预测性能都显示出显着提高，但由于缺乏处理超高维数据集的模型，性能仍然有限。本研究旨在提供一种用于预测 GFR 的两阶段神经网络方法，并建议从血液代谢物中获得的一些其他有用的生物标志物用于测量 GFR。当特征的数量远大于训练样本的数量时，它是特征收缩和神经网络的组合。结果表明，所提出的方法优于现有的方法，例如卷积神经网络和直接深度神经网络。