Multinomial Classification of Neurosurgical Operations Using Gradient Boosting and Deep Learning Algorithms

This study aimed at testing the feasibility of neurosurgical procedures classification into 100+ classes using natural language processing and machine learning. A catboost algorithm and bidirectional recurrent neural network with a gated recurrent unit showed almost the same accuracy of ∼81%, with suggestions of correct class in top 2-3 scored classes up to 98.9%. The classification of neurosurgical procedures via machine learning appears to be a technically solvable task which can be additionally improved considering data enhancement and classes verification.