Revised inverse problem algorithm-based prediction of coronary artery stenosis readings from the clinical data of patients with coronary heart diseases. (31st October 2017) - Pan, Lung-Fa; Chiu, Shao-Wen; Xiao, Min-Feng; Chen, Chia-Hui; Pan, Lung-Kwang
Abstract: Aim: Coronary artery stenosis readings were predicted in this study on the basis of clinical data for patients with coronary heart diseases using the inverse problem algorithm. Method: Five factors, including age, BSA (body surface area), MAP (mean artery pressure), sugar AC (ante cibum), and LDL-C (low-density Lipoprotein-Cholesterol) were incorporated into a nonlinear first-order regression fit analysis to develop a prediction equation with sixteen terms derived via a revised inverse problem algorithm implemented through the STATISTICA default regression fit. The clinical data acquired from ninety-three coronary heart disease patients were first normalized to the same domain range of [-1 to +1], and then processed by the above algorithm to find the compromised solution of predicted coronary artery stenosis reading. The actual reading was obtained by weighting the stenosis of three major cardiac artery branches, namely, the left anterior descending artery (LAD) (wi 0.3), left circumflex artery (LCA) (wi 0.3), and right coronary artery (RCA) (wi 0.4). Result: The derived regression fit possessed the final loss function value Φ = 3.589 and correlation coefficient r2 = 0.892 with variance of 79.55%. Accordingly, forty-five patients with similar syndromes were analyzed to verify the prediction, which exhibited a high coincidence. The LDL-C factor was dominant for the prediction of the largest coefficient in the derived equation, whereas the age factor exhibited a minor contribution to the regression fit. The attempts to reduce the number of influence factors to 4, 3 or 2 for the model simplification yielded the results, whose low linearity and high loss function values reflected their inappropriate setting. Conclusion: The algorithm proved to be an effective technique for prediction of the potential diagnosis in the medical field.