Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications

Abstract

Generalized additive models and support vector machines had good performance as risk prediction model for postoperative sepsis and AKI. Feature extraction using principal component analysis improved the predictive performance of all models.