Intraoperative neuromonitoring (IONM) has been used to help monitor the integrity of the nervous system during spine surgery.Transcranial motor-evoked potential (TcMEP) has been used lately for lower lumbar surgery to prevent nerve root injuries andalso to predict positive functional outcomes of patients. There were a number of studies that proved that the TcMEP signal’simprovement is significant towards positive functional outcomes of patients. In this paper, we explored the possibilities of using amachine learning approach to TcMEP signal to predict positive functional outcomes of patients. With 55 patients who underwentvarious types of lumbar surgeries, the data were divided into 70 : 30 and 80 : 20 ratios for training and testing of the machinelearning models. The highest sensitivity and specificity were achieved by Fine KNN of 80 : 20 ratio with 87.5% and 33.33%,respectively. In the meantime, we also tested the existing improvement criteria presented in the literature, and 50% of TcMEPimprovement criteria achieved 83.33% sensitivity and 75% specificity. But the rigidness of this threshold method proved unreliablein this study when different datasets were used as the sensitivity and specificity dropped. The proposed method by using machinelearning has more room to advance with a larger dataset and various signals’ features to choose from

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