Inverse Problem Algorithm Application to Semi-Quantitative Analysis of 272 Patients With Ischemic Stroke Symptoms: Carotid Stenosis Risk Assessment for Five Risk Factors

Abstract

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.

Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics

“…In doing so, four out of seven risk factors were preset as average values to imply their general behavior in the CAD patient group because of their minor contributions to the accuracy of the SYNTAX score (cf. Table 3 ) [ 19 , 20 ]. The four minor factors were preset as MAP (0.0), glucose AC (0.68), cTnI (0.90), and CRP (0.74).…”

Potential risk quantification from multiple biological factors via the inverse problem algorithm as an artificial intelligence tool in clinical diagnosis

“…Noteworthy is that only in the study of Pan et al [1], the rank of constant (rank 14/16) was less than any others, namely, ranks 6/16, 7/29, 5/16, 5/22, and 6/22, respectively in the studies [2][3][4][5][6]. Thus, the optimized loss function, Φ (cf.…”

“…Accordingly, serum creatinine can be precisely estimated after CM administration [1] since r 2 reaches as high as 0.986 (1.00 is the maximal), and even in the worst case among all, r 2 still holds 0.795 in the study of Pan et al [2]. The accurate estimation of either coronary artery or carotid stenosis helps cardiac doctors to grasp the principle in clinical diagnosis before having any interventional examination [2,4].…”

“…Thus, the preset scenario describes a general case of patients. Figure 5 aligned in seven frames along the Z-, X-, and Y-axes, respectively [4]. The benefit of drawing a ladder diagram is that it can visualize the IPA-provided information and furnish the medical staff with an instant quantified index for referring before having precise computation from the STATISTICA program.…”

“…The IPA uses mathematical calculations to estimate the expectation value of a specific index according to patient risk factor groups. Furthermore, the contributions of particular risk factors or their cross-interactions can be evaluated and ranked by their importance [ 1 , 2 , 3 , 4 , 5 , 6 ]. Unlike the Taguchi optimization algorithm, which is used to optimize one purpose from a combination of multiple factors [ 7 , 8 , 9 , 10 ], IPA directly estimates the expectation value from a group of data.…”