区分胶质肉瘤和胶质母细胞瘤:利用 PEACE 和 XGBoost 处理超高维度混杂因素数据集的新方法

Life Pub Date : 2024-07-16 DOI:10.3390/life14070882
A. Saki, U. Faghihi, Ismaila Baldé
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引用次数: 0

摘要

在这项研究中,我们使用了最近开发的一种因果关系方法,即概率易变因果效应(PEACE),来区分胶质肉瘤(GSM)和胶质母细胞瘤(GBM)。我们的方法采用了一种因果度量方法,将概率易变因果效应(PEACE)与 XGBoost(即梯度提升算法)相结合。以往的研究通常在进行因果分析前依赖于统计模型来减少数据集的维度,而我们的方法则不同,它使用了完整的数据集、PEACE 和 XGBoost 算法。PEACE 提供了对直接因果效应的全面测量,适用于连续和离散变量。我们的方法提供正负两个版本的 PEACE 及其平均值,以计算放射学特征对代表肿瘤类型(GSM 或 GBM)的变量的正负因果效应。在我们的模型中,PEACE 及其变体都带有度数 d,度数从 0 到 1 不等,它反映了事件稀有性和频率的重要性。通过将 PEACE 与 XGBoost 结合使用,我们对数据集特征中的因果关系有了细致入微的了解,从而有助于准确区分 GSM 和 GBM。为了评估 XGBoost 模型,我们使用了交叉验证,获得了 83% 的平均准确率和 0.130 的平均模型 MSE。鉴于列数较多而行数较少,这一性能非常显著(代码在 GitHub 上)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost to Deal with Datasets with Ultra-High Dimensional Confounders
In this study, we used a recently developed causal methodology, called Probabilistic Easy Variational Causal Effect (PEACE), to distinguish gliosarcoma (GSM) from glioblastoma (GBM). Our approach uses a causal metric which combines Probabilistic Easy Variational Causal Effect (PEACE) with the XGBoost, or eXtreme Gradient Boosting, algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach uses the complete dataset with PEACE and the XGBoost algorithm. PEACE provides a comprehensive measurement of direct causal effects, applicable to both continuous and discrete variables. Our method provides both positive and negative versions of PEACE together with their averages to calculate the positive and negative causal effects of the radiomic features on the variable representing the type of tumor (GSM or GBM). In our model, PEACE and its variations are equipped with a degree d which varies from 0 to 1 and it reflects the importance of the rarity and frequency of the events. By using PEACE with XGBoost, we achieved a detailed and nuanced understanding of the causal relationships within the dataset features, facilitating accurate differentiation between GSM and GBM. To assess the XGBoost model, we used cross-validation and obtained a mean accuracy of 83% and an average model MSE of 0.130. This performance is notable given the high number of columns and low number of rows (code on GitHub).
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