基于机器学习的预测模型：血清γKlotho水平对冠心病易感性的影响
Machine Learning-Based Prediction Model for Predicting the Effect of the Serum γKlotho Level on Susceptibility to Coronary Heart Disease.
摘要(中文译文)
本研究探讨血清γKlotho水平与冠心病(coronary heart disease, CHD)风险之间的关系,并构建机器学习模型用于CHD预测。共纳入1435名受试者进行分析,并随机分为训练组(n=969,70%)和验证组(n=466,30%)。训练组用于单因素回归分析。随后,采用最小绝对收缩和选择算子(least absolute shrinkage and selection operator, LASSO)回归筛选CHD的独立危险因素。利用CHD的独立危险因素,构建了九种机器学习模型,通过评估选出最佳模型,并通过决策曲线分析(decision curve analysis, DCA)对模型进行验证。与CHD风险独立相关的因素包括年龄、血清γKlotho水平、LDL-C、性别、糖尿病、高血压和吸烟状况。我们利用这些危险因素构建了九种常用的机器学习模型。在所有模型中,RF模型更为合适;因此,我们对该模型进行了可视化和验证,结果显示其具有良好的临床应用前景。血清γKlotho水平是一种新型生物标志物,与CHD风险呈正相关。此外,RF模型能更好地预测CHD风险,RF模型更适合用于临床预测CHD风险。
Abstract
This study investigates the relationship between serum γKlotho levels and coronary heart disease (CHD) risk and develops a machine learning model for CHD prediction. A total of 1435 subjects were enrolled for analysis and randomized as training (n = 969, 70%) or validation (n = 466, 30%) group. The training group was used for univariate regression. Thereafter, least absolute shrinkage and selection operator (LASSO) regression was conducted for selecting independent risk factors for CHD. Using independent risk factors for CHD, nine machine learning models were developed, the best model was selected by evaluating them, and the model was validated by decision curve analysis (DCA). The factors independently associated with CHD risk were age, the serum level of γKlotho, LDL-C, sex, diabetes, hypertension, and smoking status. We used these risk factors to construct nine popular machine-learning models. Among all models, the RF model was better appropriate; thus, we visualized and validated this model, which showed promising clinical application. Serum γKlotho levels are novel biomarker which positively related to CHD risk. Additionally, the RF model can better predict the risk of CHD, and RF model is better appropriate to predicting the CHD risk in clinics.
如何引用
AMA
Zi-Tong Guo, Xiao-Lin Yu, Hui Cheng, Tuersunjiang Naman. Machine Learning-Based Prediction Model for Predicting the Effect of the Serum γKlotho Level on Susceptibility to Coronary Heart Disease.. Vascular health and risk management. 2025; doi:10.2147/VHRM.S508351.
APA
Zi-Tong Guo, Xiao-Lin Yu, Hui Cheng, Tuersunjiang Naman (2025). Machine Learning-Based Prediction Model for Predicting the Effect of the Serum γKlotho Level on Susceptibility to Coronary Heart Disease.. Vascular health and risk management. https://doi.org/10.2147/VHRM.S508351
GB/T 7714
Zi-Tong Guo, Xiao-Lin Yu, Hui Cheng, Tuersunjiang Naman. Machine Learning-Based Prediction Model for Predicting the Effect of the Serum γKlotho Level on Susceptibility to Coronary Heart Disease.[J]. Vascular health and risk management, 2025 doi:10.2147/VHRM.S508351.
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