Applying Artificial Neural Network on the Prognosis ofGuillain-Barré Syndrome

• Main Authors: En-Chi Hsieh, 謝恩綺
• Other Authors: 邱泓文
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/333yb5

碩士 === 臺北醫學大學 === 醫學資訊研究所 === 103 === Background: Guillain-Barré syndrome is a rare disorder. The disease symptoms include upper respiratory tract infection and gastroenteritis. The physical symptoms occur double vision and limb numbness. Diagnosed with Guillain-Barré syndrome, patients conduct treatment of plasma exchange or intravenous immunoglobulin. Nearly 80% of patients recover fully, 10% -20% of patients have permanent neurological sequelae, 5% -10% patients will failure to die. Method: The study applies logistic regression and artificial neural network to construct predicted models for the prognosis of Guillain-Barré syndrome. The study uses Chi-square test and T test to analysis the relation between factors and the prognosis of Guillain-Barré syndrome. Result: The study totally includes 72 patients with Guillain-Barré syndrome, and 60 patients recover well. The rate of prognosis for recovery is 80%. Analysis for the predicted factors, patients used mechanical ventilation and patients’ MRC scores of lower limbs are significant factors for the prognosis of Guillain-Barré syndrome. Applying logistic regression to build predicted models, the model of all possible regression procedure that the predicted accuracy is 0.944 and the model of stepwise regression procedure is 0.93. Then, adjusted samples apply artificial neural network to build predicted models. Selecting all eigenvalues model with 100% test set performance, the predicted accuracy is 1. Selecting significant eigenvalues model with 96% test set performance, the predicted accuracy is 0.858. The late of research removes the interferon variable of patients used mechanical ventilation. The predicted performance of rebuild models is obviously decreased. Conclusion: Comprehensive comparison of four predicted models, the study shows results. The structured method to choose all predicted factors that model predicted performance is better than the structured method to choose significant predicted factors. Applying the analysis method of artificial neural network, the model with all eigenvalues that predicted accuracy and model performance are the best in the study.