An optimized machine learning method for predicting wogonin therapy for the treatment of pulmonary hypertension
An optimized machine learning method for predicting wogonin therapy for the treatment of pulmonary hypertension
复制标题
DOI：
10.1016/j.compbiomed.2023.107293
复制
发表时间：
2023-08-15
影响因子：
7.7
通讯作者：
Wu，Peiliang
中科院分区：
文献类型：
--
作者：
Li，Yupeng;Fu，Yujie;Wu，Peiliang
肺动脉高压（PH）是一种以肺血管阻力降低和肺血管收缩为特征的疾病，可导致右心衰竭和功能障碍。因此，预防PH并在治疗前监测其进展至关重要。汉黄芩素（Wogonin）是从中药黄芩（EscherialiabaicalensisGeorgi）叶中提取的一种有效成分，具有显著的药理活性。在这项研究中，我们研究了有效的汉黄芩素在减轻小鼠右心导管术和苏木精-伊红（HE）染色的PH的进展。作为一种替代方案，以尽量减少伤害小动物的可能性，我们提出了一种科学有效的特征选择方法（BSCDWOA-KELM），这将使我们能够开发一种新的更简单的非侵入性预测方法汉黄芩素治疗PH。在这种方法中，我们使用建议的增强鲸鱼优化器（SCDWOA）结合内核极端学习机（KELM）。首先，我们让SCDWOA在IEEE CEC 2014基准函数集上进行全局优化实验，以验证其核心优势。最后，使用BSCDWOA-KELM对12个公共数据集和PH数据集进行了特征选择实验。实验结果表明，该算法具有较好的全局寻优性能。同时，提出的二进制SCDWOA（BSCDWOA）显着提高了KELM的分类数据的能力。通过利用BSCDWOA-KELM，可以在肺动脉高压数据集中有效地筛选红细胞（RBC）、血红蛋白（HGB）、淋巴细胞百分比（LYM%）、红细胞压积（HCT）和红细胞分布宽度-尺寸分布（RDW-SD）等关键指标，其最重要的一点是其精度大于0.98。因此，BSCDWOA-KELM可以用于预测汉黄芩素治疗肺动脉高压的简单和无创的方式。
Human health is at risk from pulmonary hypertension (PH), characterized by decreased pulmonary vascular resistance and constriction of the pulmonary vessels, resulting in right heart failure and dysfunction. Thus, preventing PH and monitoring its progression before treating it is vital. Wogonin, derived from the leaves of Scutellaria baicalensis Georgi, exhibits remarkable pharmacological activity. In this study, we examined the effectiveness of wogonin in mitigating the progression of PH in mice using right heart catheterization and hematoxylin-eosin (HE) staining. As an alternative to minimize the possibility of harming small animals, we present a scientifically effective feature selection method (BSCDWOA-KELM) that will allow us to develop a novel simpler noninvasive prediction method for wogonin in treating PH. In this method, we use the proposed enhanced whale optimizer (SCDWOA) in conjunction with the kernel extreme learning machine (KELM). Initially, we let SCDWOA perform global optimization experiments on the IEEE CEC2014 benchmark function set to verify its core advantages. Lastly, 12 public and PH datasets are examined for feature selection experiments using BSCDWOA-KELM. As shown in the experimental results for global optimization, the proposed SCDWOA has better convergence performance. Meanwhile, the proposed binary SCDWOA (BSCDWOA) significantly improves the ability of KELM to classify data. By utilizing the BSCDWOA-KELM, key indicators such as the Red blood cell (RBC), the Haemoglobin (HGB), the Lymphocyte percentage (LYM%), the Hematocrit (HCT), and the Red blood cell distribution width-size distribution (RDW-SD) can be efficiently screened in the Pulmonary hypertension dataset, and one of its most essential points is its accuracy of greater than 0.98. Consequently, the BSCDWOA-KELM introduced in this study can be used to predict wogonin therapy for treating pulmonary hypertension in a simple and noninvasive manner.