Prediction of patient's neurological recovery from cervical spinal cord injury through XGBoost learning approach

Due to the diversity of patient characteristics, therapeutic approaches, and radiological findings, it can be challenging to predict outcomes based on neurological consequences accurately within cervical spinal cord injury (SCI) entities and based on machine learning (ML) technique. Accurate neurological outcomes prediction in the patients suffering with cervical spinal cord injury is challenging due to heterogeneity existing in patient characteristics and treatment strategies. Machine learning algorithms are proven technology for achieving greater prediction outcomes. Thus, the research employed machine learning model through extreme gradient boosting (XGBoost) for attaining superior accuracy and reliability followed with other MI algorithms for predicting the neurological outcomes. Besides, it generated a model of a data-driven approach with extreme gradient boosting to enhance fault detection techniques (XGBoost) efficiency rate. To forecast improvements within functionalities of neurological systems, the status has been monitored through motor position (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) followed by the method of prediction employing XGBoost, combined with decision tree for regression logistics. Thus, with the proposed XGBoost approach, the enhanced accuracy in reaching the outcome is 81.1%, and from other models such as decision tree (80%) and logistic regression (82%), in predicting outcomes of neurological improvements within cervical SCI patients. Considering the AUC, the XGBoost and decision tree valued with 0.867 and 0.787, whereas logistic regression showed 0.877. Therefore, the application of XGBoost for accurate prediction and decision-making in the categorization of pre-treatment in patients with cervical SCI has reached better development with this study.