This study proposes the inverse problem algorithm (IPA) with five risk factors applied to the semi-quantitative analysis of carotid stenosis 272 patients with suspected ischemic stroke. The IPA is known to provide a substantiated machine learning-based prediction of the expected outcomes by solving an inverse matrix of variable coefficients. In case of carotid stenosis prediction, such risk factors as patient’s age, mean arterial pressure (MAP), glucose AC, low-density lipoprotein-cholesterol (LDL-C), and C-Reactive protein (CRP) were assessed for the main group of 217 patients. Their results were processed by the STATISTICA program with a customized loss function ([Formula: see text]), yielding the first-order nonlinear semi-empirical formula with 16 terms. The loss function was calculated via the total mismatch between the theoretical predictions and true carotid stenosis cases (%) for all 217 patients. Thus, the carotid stenosis (%) compromised solution array [[Formula: see text]] was optimized using [Formula: see text] individual data points via the proposed algorithm. The results showed a complete regression with loss function [Formula: see text]=2.3543, variance [Formula: see text]=87.46%, and correlation coefficient [Formula: see text]. The reference group of 55 more patients with the same preliminary diagnosis and symptoms was selected to validate the method predictive feasibility, which was found quite satisfactory. The decreasing order of three dominant risk factors was as follows: CRP, glucose AC, and MAP, whereas age and LDL-C weakly influenced the program computation results. The IPA showed a strong convergence by its default characteristic. The reduction of the number of variables in computation deteriorated the prediction accuracy, exhibiting the algorithm’s high sensitivity to the number of variables.

中文翻译：

---

逆问题算法在 272 例缺血性卒中症状患者的半定量分析中的应用：五个风险因素的颈动脉狭窄风险评估

本研究提出了具有五个危险因素的逆问题算法 (IPA)，应用于对 272 例疑似缺血性卒中的颈动脉狭窄患者进行半定量分析。众所周知，IPA 通过求解可变系数的逆矩阵来提供基于机器学习的经证实的预期结果预测。在颈动脉狭窄预测的情况下，主要评估患者年龄、平均动脉压 (MAP)、葡萄糖 AC、低密度脂蛋白胆固醇 (LDL-C) 和 C 反应蛋白 (CRP) 等危险因素217 名患者。他们的结果由具有自定义损失函数的 STATISTICA 程序处理（[公式：见正文]），产生具有 16 项的一阶非线性半经验公式。损失函数是通过所有 217 名患者的理论预测与真实颈动脉狭窄病例 (%) 之间的总不匹配来计算的。因此，颈动脉狭窄 (%) 受损的解决方案阵列 [[公式：参见文本]] 通过所提出的算法使用 [公式：参见文本] 各个数据点进行了优化。结果显示完全回归，损失函数[公式：见文]=2.3543，方差[公式：见文]=87.46%，相关系数[公式：见文]。选取55名以上具有相同初步诊断和症状的患者作为参考组，验证该方法预测的可行性，结果比较满意。三个主要危险因素的降序如下：CRP、葡萄糖 AC 和 MAP，而年龄和 LDL-C 对程序计算结果的影响较弱。IPA 通过其默认特性表现出很强的收敛性。计算中变量数量的减少降低了预测精度，表现出算法对变量数量的高度敏感性。