The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data.

ABSTRACT: Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high-cost patients in the following year. Therefore, we exploited routinely collected sickness funds claims and cost data of the years 2016, 2017 and 2018. Various specifications of each algorithm were trained and cross-validated on training data (n = 20,984) with claims and cost data from 2016 and outcomes from 2017. The best performing specifications of each algorithm were selected based on validation dataset performance. For performance comparison, selected models were applied to unforeseen data with features of the year 2017 and outcomes of the

SUBMITTER: Langenberger B 

PROVIDER: S-EPMC9847900 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature