Sharma Kirti, Tiwari Pawan K, Sinha S K
Department of Physics, Birla Institute of Technology, Mesra, Ranchi 835215, India.
J Chem Inf Model. 2025 Feb 24;65(4):1736-1746. doi: 10.1021/acs.jcim.4c01423. Epub 2025 Feb 5.
Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabolism and become chronic upon negligence. Diabetes is one of the key factors that is interlinked with a plethora of diseases. Health management can be achieved through balanced diet, physical exercise, and periodic examination of blood glucose level and hematocrit volume. Our study developed a model to estimate the hematocrit volume (red blood cells) from the correlation of the glucose concentration obtained from a glucometer by employing machine learning techniques. This Article explores the prediction of hematocrit volume in whole blood by applying various machine learning (ML) models such as linear regression (LR), support vector regressor (SVR), decision tree (DT), random forest regressor (RFR), artificial neural network (ANN), and extreme gradient boosting regressor model (XGBoost). We used amperometric signals generated from an electrochemical glucose sensor or glucose strip, which produces current values on glucose concentration. We estimated the hematocrit volume via processing of the amperometric signals to enhance diagnostic capabilities with the least error in the field of biomedical signal processing. The ML models were trained on the data set comprising 80% training set and 20% testing set in the Python programming language. The models were evaluated based on the metrics such as R-squared (R), mean squared error (MSE), and root mean squared error (RMSE) values, and their reliability was assessed through the three validation mechanisms, namely, the relative error, K-fold cross-validation, and analysis of confidence interval. We observed that the XGBoost regression results were comparatively better than the LR and ANN results as corroborated through reliability analysis. It was concluded that XGBoost demonstrated 15% relative error between actual and predicted data and 68% accuracy with 6% standard deviation in the prediction obtained via a 5-fold cross-validation technique. The XGBoost model demonstrates comparatively better performance in terms of flexibility in tuning and interpretability options, which make it suitable for the regression task in the predictive biomedical analytics.
心血管疾病、糖尿病等生活方式疾病会影响生理代谢，若不加以重视就会发展成慢性病。糖尿病是与多种疾病相互关联的关键因素之一。通过均衡饮食、体育锻炼以及定期检测血糖水平和血细胞比容，可以实现健康管理。我们的研究利用机器学习技术，基于血糖仪测得的葡萄糖浓度相关性，开发了一种估算血细胞比容（红细胞）的模型。本文通过应用各种机器学习（ML）模型，如线性回归（LR）、支持向量回归器（SVR）、决策树（DT）、随机森林回归器（RFR）、人工神经网络（ANN）和极端梯度提升回归器模型（XGBoost），探索全血中血细胞比容的预测。我们使用了电化学葡萄糖传感器或葡萄糖试纸产生的安培信号，该信号会根据葡萄糖浓度产生电流值。我们通过处理安培信号来估算血细胞比容，以在生物医学信号处理领域以最小误差提高诊断能力。这些ML模型在Python编程语言中使用包含80%训练集和20%测试集的数据集进行训练。基于决定系数（R）、均方误差（MSE）和均方根误差（RMSE）值等指标对模型进行评估，并通过相对误差、K折交叉验证和置信区间分析这三种验证机制评估其可靠性。通过可靠性分析证实，我们观察到XGBoost回归结果比LR和ANN的结果相对更好。得出的结论是，通过5折交叉验证技术获得的预测中，XGBoost在实际数据与预测数据之间显示出15%的相对误差，准确率为68%，标准差为6%。XGBoost模型在调优灵活性和可解释性选项方面表现出相对更好的性能，这使其适用于预测性生物医学分析中的回归任务。
