Gradient Boosting Machine Learning Algorithm Research Articles

Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study.

Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis using machine learning algorithms may generate models that predict EPC better than conventional statistical methods. To train such a model and to assess its predictive ability. This cohort study included patients from an ongoing prospective patient registry at a single tertiary care center with an intracranial tumor that underwent elective neurosurgery between June 2015 and May 2017. EPC were categorized based on the Clavien-Dindo classification score. Conventional statistical methods and different machine learning algorithms were used to predict EPC using preoperatively available patient, clinical, and surgery-related variables. The performance of each model was derived from examining classification performance metrics on an out-of-sample test dataset. EPC occurred in 174 (26%) of 668 patients included in the analysis. Gradient boosting machine learning algorithms provided the model best predicting the probability of an EPC. The model scored an accuracy of 0.70 (confidence interval [CI] 0.59-0.79) with an area under the curve (AUC) of 0.73 and a sensitivity and specificity of 0.80 (CI 0.58-0.91) and 0.67 (CI 0.53-0.77) on the test set. The conventional statistical model showed inferior predictive power (test set: accuracy: 0.59 (CI 0.47-0.71); AUC: 0.64; sensitivity: 0.76 (CI 0.64-0.85); specificity: 0.53 (CI 0.41-0.64)). Using gradient boosting machine learning algorithms, it was possible to create a prediction model superior to conventional statistical methods. While conventional statistical methods favor patients' characteristics, we found the pathology and surgery-related (histology, anatomical localization, surgical access) variables to be better predictors of EPC.

An Innovative Tree Gradient Boosting Method Based Image Object detection from a Uniform Background Scene

Object detection is a wide area problem domain in the field of computer and machine vision. Complex background adds challenge and error margin as well to the problem significantly lot algorithms for object detection are hard to comply with occlusion and pixel bending moment affect. In this paper a highly robust algorithm ORBTRIAN for a less resolution image has been proposed and implemented using ORB detection with gradient boosting machine learning algorithm. The work has been compared with Adaboost and Surf based technology. The analysis shows 3.8% increase in performance of earlier model. The feature points extracted from ORB method are further processed to reduce the processing further. Only those points are selected which are triangularly farthest from centroid of it and only 1 point of feature selected. Thus the result is around 28%, much faster than earlier computation. The tree based GB has been implemented in this algorithm. With more number of feature points more classes need to be recognized and hence the computations performed is required an unreasonable amount of effort and time. So some nearby classes are assigned at same level using our algorithm to reduce the number of tree nodes. Overall performance of the proposed algorithm shows a significant increase in efficiency in computation time.